docstr: After the evaluation phase of each turn, update history trials according to the performance
  
  Parameters
  ----------
  pid : int
      The process id which runs the evaluation
  cur_trainer : ..train.BaseTrainer
      The trainer used in evaluation, containing the HPs
  perf : float
      The performance of the HP
  is_higher_better : bool
      True if higher perf is better
docstr_tok: ["After","the","evaluation","phase","of","each","turn",",","update","history","trials","according","to","the","performance","Parameters","--","--","--","--","--","pid",":","int","The","process","id","which","runs","the","evaluation","cur_trainer",":","..","train.BaseTrainer","The","trainer","used","in","evaluation",",","containing","the","HPs","perf",":","float","The","performance","of","the","HP","is_higher_better",":","bool","True","if","higher","perf","is","better"]
code: def _update_trials(self, pid, cur_trainer, perf, is_higher_better):
      """
      After the evaluation phase of each turn, update history trials according to the performance
  
      Parameters
      ----------
      pid : int
          The process id which runs the evaluation
      cur_trainer : ..train.BaseTrainer
          The trainer used in evaluation, containing the HPs
      perf : float
          The performance of the HP
      is_higher_better : bool
          True if higher perf is better
      """
      pass
  
imports: from ...utils import get_logger
  import hyperopt
  import time
  import math
  import dill
  import multiprocessing
  import copy
  import random
label_desc: Use image node_id 15 for calling the BaseHPOptimizer obj's underlying member method code with example usage: obj._update_trials(pid, cur_trainer, perf, is_higher_better) without return types