code: def _complete_trial_to_skopt_observation(
      self, study: Study, trial: FrozenTrial
  ) -> Tuple[List[Any], float]:
      param_values = []
      for name, distribution in sorted(self._search_space.items()):
          param_value = trial.params[name]
  
          if isinstance(distribution, distributions.FloatDistribution):
              if distribution.step is not None:
                  param_value = (
                      param_value - distribution.low
                  ) // distribution.step
          elif isinstance(distribution, distributions.IntDistribution):
              if not distribution.log:
                  param_value = (
                      param_value - distribution.low
                  ) // distribution.step
  
          param_values.append(param_value)
  
      value = trial.value
      assert value is not None
  
      if study.direction == StudyDirection.MAXIMIZE:
          value = -value
  
      return param_values, value
  
imports: import copy
  import random
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Sequence
  from typing import Tuple
  import warnings
  import numpy
  import optuna
  from optuna import distributions
  from optuna import samplers
  from optuna._deprecated import deprecated_class
  from optuna._imports import try_import
  from optuna.exceptions import ExperimentalWarning
  from optuna.samplers import BaseSampler
  from optuna.search_space import IntersectionSearchSpace
  from optuna.study import Study
  from optuna.study._study_direction import StudyDirection
  from optuna.trial import FrozenTrial
  from optuna.trial import TrialState
label_desc: Use image node_id 5 for calling the _Optimizer obj's underlying member method code with example usage: obj._complete_trial_to_skopt_observation(study, trial) and returns: param_values,  value