code: def test_get_param_importance_target_is_none_and_study_is_multi_obj(
      storage_mode: str,
      evaluator_init_func: Callable[[], BaseImportanceEvaluator],
  ) -> None:
      def objective(trial: Trial) -> tuple[float, float]:
          x1 = trial.suggest_float("x1", 0.1, 3)
          x2 = trial.suggest_float("x2", 0.1, 3, log=True)
          x3 = trial.suggest_float("x3", 0, 3, step=1)
          x4 = trial.suggest_int("x4", -3, 3)
          x5 = trial.suggest_int("x5", 1, 5, log=True)
          x6 = trial.suggest_categorical("x6", [1.0, 1.1, 1.2])
          if trial.number % 2 == 0:
              # Conditional parameters are ignored unless `params` is specified and is not `None`.
              x7 = trial.suggest_float("x7", 0.1, 3)
  
          value = x1**4 + x2 + x3 - x4**2 - x5 + x6
          if trial.number % 2 == 0:
              value += x7
          return value, 0.0
  
      with StorageSupplier(storage_mode) as storage:
          study = create_study(
              directions=["minimize", "minimize"], storage=storage
          )
          study.optimize(objective, n_trials=3)
  
          with pytest.raises(ValueError):
              get_param_importances(
                  study, evaluator=evaluator_init_func()
              )
  
imports: from __future__ import annotations
  from collections.abc import Callable
  from typing import Any
  from typing import Type
  import numpy
  import pytest
  import optuna
  from optuna import samplers
  from optuna.exceptions import ExperimentalWarning
  from optuna.importance import BaseImportanceEvaluator
  from optuna.importance import FanovaImportanceEvaluator
  from optuna.importance import get_param_importances
  from optuna.importance import MeanDecreaseImpurityImportanceEvaluator
  from optuna.samplers import RandomSampler
  from optuna.study import create_study
  from optuna.testing.objectives import pruned_objective
  from optuna.testing.storages import STORAGE_MODES
  from optuna.testing.storages import StorageSupplier
  from optuna.trial import Trial
label_desc: Use image node_id 1 for calling a global function with example usage: test_get_param_importance_target_is_none_and_study_is_multi_obj(storage_mode, evaluator_init_func) and returns: value,  int