code: def test_sampling(storage_mode: str) -> None:
      def objective(trial: BaseTrial) -> float:
          a = trial.suggest_float("a", 0.0, 10.0)
          b = trial.suggest_float("b", 0.1, 10.0, log=True)
          c = trial.suggest_float("c", 0.0, 10.0, step=1.0)
          d = trial.suggest_int("d", 0, 10)
          e = trial.suggest_categorical("e", [0, 1, 2])
          f = trial.suggest_int("f", 1, 10, log=True)
  
          return a + b + c + d + e + f
  
      with StorageSupplier(storage_mode) as storage:
          study = create_study(storage=storage)
          study.optimize(objective, n_trials=1)
  
          best_trial = study.best_trial
  
          # re-evaluate objective with the best hyperparameters
          v = objective(best_trial)
  
          assert v == best_trial.value
  
imports: from __future__ import annotations
  import copy
  import datetime
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Tuple
  import pytest
  from optuna import create_study
  from optuna.distributions import BaseDistribution
  from optuna.distributions import FloatDistribution
  from optuna.distributions import IntDistribution
  from optuna.testing.storages import STORAGE_MODES
  from optuna.testing.storages import StorageSupplier
  import optuna.trial
  from optuna.trial import BaseTrial
  from optuna.trial import create_trial
  from optuna.trial import FrozenTrial
  from optuna.trial import TrialState
label_desc: Use image node_id 5 for calling a global function with example usage: test_sampling(storage_mode) and returns: unknown