docstr: Parameters
  ----------
  parameter_id: int
      the index of the parameter
  parameters : dict
      chosen architecture and parameters
  result : float
      final metric of the chosen one
docstr_tok: ["Parameters","--","--","--","--","--","parameter_id",":","int","the","index","of","the","parameter","parameters",":","dict","chosen","architecture","and","parameters","result",":","float","final","metric","of","the","chosen","one"]
code: def __init__(self, parameter_id, parameters, result):
      """
      Parameters
      ----------
      parameter_id: int
          the index of the parameter
      parameters : dict
          chosen architecture and parameters
      result : float
          final metric of the chosen one
      """
      self.parameter_id = parameter_id
      self.parameters = parameters
      self.result = result
  
imports: import copy
  import logging
  import random
  from collections import deque
  from schema import Schema, Optional
  import nni
  from nni.tuner import Tuner
  from nni import ClassArgsValidator
  from nni.utils import OptimizeMode, extract_scalar_reward
label_desc: Use image node_id 1 to create a new FinishedIndividual object  with example: obj = FinishedIndividual(parameter_id, parameters, result)