class_docstr: An abstract HPOptimizer using for `advisor` package.
  See https://github.com/tobegit3hub/advisor for more information
  
  Attributes
  ----------
  method : .suggestion.AbstractSuggestionAlgorithm
      The algorithm class in `suggestion`
  max_evals : int
      The max rounds of evaluating HPs
  
  Methods
  -------
  optimize
      Optimize the HP by the method within give model and HP space
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docstr: See .base.BaseHPOptimizer._get_suggestion
docstr_tok: ["See",".base.BaseHPOptimizer._get_suggestion"]
code: def _get_suggestion(self, pid):
      """
      See .base.BaseHPOptimizer._get_suggestion
      """
      new_trials = self.method.get_new_suggestions(
          self.study, trials=self.trials, number=1
      )
  
      new_trial = new_trials[0]
      new_parameter_values_json = json.loads(new_trial.parameter_values)
      decoded_json, trial_para = self._decode_para(
          new_parameter_values_json
      )
      new_trial.parameter_values = json.dumps(trial_para)
      self.new_trials[pid] = new_trial
      self.xs[pid] = decoded_json
      return decoded_json
  
file_path: AutoGL/autogl/module/hpo/advisorbase.py
imports: import time
  import json
  import math
  from tqdm import trange
  from .suggestion.models import Study
  from .base import BaseHPOptimizer, TimeTooLimitedError
  from .suggestion.algorithm.random_search import RandomSearchAlgorithm
  from .suggestion.algorithm.mocmaes import MocmaesAlgorithm
label_desc: Use image node_id 4 for calling the AdvisorBaseHPOptimizer obj's underlying member method code with example usage: obj._get_suggestion(pid) and returns: decoded_json