code: def load_study(
      study_name: str,
      storage: Union[str, BaseStorage],
      sampler: Optional[
          "multi_objective.samplers.BaseMultiObjectiveSampler"
      ] = None,
  ) -> "multi_objective.study.MultiObjectiveStudy":
      """Load the existing :class:`MultiObjectiveStudy` that has the specified name.
  
      Example:
  
          .. testsetup::
  
              import os
  
              if os.path.exists("example.db"):
                  raise RuntimeError("'example.db' already exists. Please remove it.")
  
          .. testcode::
  
              import optuna
  
  
              def objective(trial):
                  # Binh and Korn function.
                  x = trial.suggest_float("x", 0, 5)
                  y = trial.suggest_float("y", 0, 3)
  
                  v0 = 4 * x**2 + 4 * y**2
                  v1 = (x - 5) ** 2 + (y - 5) ** 2
                  return v0, v1
  
  
              study = optuna.multi_objective.create_study(
                  directions=["minimize", "minimize"],
                  study_name="my_study",
                  storage="sqlite:///example.db",
              )
              study.optimize(objective, n_trials=3)
  
              loaded_study = optuna.multi_objective.study.load_study(
                  study_name="my_study", storage="sqlite:///example.db"
              )
              assert len(loaded_study.trials) == len(study.trials)
  
          .. testcleanup::
  
              os.remove("example.db")
  
      Args:
          study_name:
              Study's name. Each study has a unique name as an identifier.
          storage:
              Database URL such as ``sqlite:///example.db``. Please see also the documentation of
              :func:`~optuna.multi_objective.study.create_study` for further details.
          sampler:
              A sampler object that implements background algorithm for value suggestion.
              If :obj:`None` is specified,
              :class:`~optuna.multi_objective.samplers.RandomMultiObjectiveSampler` is used
              as the default. See also :class:`~optuna.multi_objective.samplers`.
  
      Returns:
          A :class:`~optuna.multi_objective.study.MultiObjectiveStudy` object.
      """
  
      mo_sampler = (
          sampler
          or multi_objective.samplers.RandomMultiObjectiveSampler()
      )
      sampler_adapter = (
          multi_objective.samplers._MultiObjectiveSamplerAdapter(
              mo_sampler
          )
      )
  
      study = _load_study(
          study_name=study_name,
          storage=storage,
          sampler=sampler_adapter,
      )
  
      return MultiObjectiveStudy(study)
  
imports: import types
  from typing import Any
  from typing import Callable
  from typing import Container
  from typing import Dict
  from typing import Iterable
  from typing import List
  from typing import Optional
  from typing import Sequence
  from typing import Tuple
  from typing import Type
  from typing import Union
  from optuna import logging
  from optuna import multi_objective
  from optuna._deprecated import deprecated_class
  from optuna._deprecated import deprecated_func
  from optuna.pruners import NopPruner
  from optuna.storages import BaseStorage
  from optuna.study import create_study
  from optuna.study import load_study
  from optuna.study import Study
  from optuna.study._study_direction import StudyDirection
  from optuna.trial import FrozenTrial
  from optuna.trial import Trial
  from optuna.trial import TrialState
label_desc: Use image node_id 2 for calling a global function with example usage: load_study(study_name, storage, sampler) and returns: MultiObjectiveStudy