code: def test_get_pareto_front_info_infer_n_targets() -> None:
      study = optuna.create_study(directions=["minimize", "minimize"])
      assert _get_pareto_front_info(study).n_targets == 2
  
      study = optuna.create_study(directions=["minimize"] * 5)
      assert (
          _get_pareto_front_info(
              study,
              target_names=["target1", "target2"],
              targets=lambda _: [0.0, 1.0],
          ).n_targets
          == 2
      )
  
      study = optuna.create_study(directions=["minimize"] * 5)
      study.optimize(lambda _: [0] * 5, n_trials=1)
      assert (
          _get_pareto_front_info(
              study, targets=lambda _: [0.0, 1.0]
          ).n_targets
          == 2
      )
  
      study = optuna.create_study(directions=["minimize"] * 2)
      with pytest.raises(ValueError):
          _get_pareto_front_info(study, targets=lambda _: [0.0, 1.0])
  
imports: from __future__ import annotations
  from io import BytesIO
  from typing import Any
  from typing import Callable
  from typing import Sequence
  import warnings
  import pytest
  import optuna
  from optuna import create_study
  from optuna import create_trial
  from optuna.distributions import FloatDistribution
  from optuna.study.study import Study
  from optuna.trial import FrozenTrial
  from optuna.visualization import plot_pareto_front
  import optuna.visualization._pareto_front
  from optuna.visualization._pareto_front import _get_pareto_front_info
  from optuna.visualization._pareto_front import _ParetoFrontInfo
  from optuna.visualization._plotly_imports import go
  from optuna.visualization._utils import COLOR_SCALE
  from optuna.visualization.matplotlib._matplotlib_imports import plt
  import optuna.visualization.matplotlib._pareto_front
label_desc: Use image node_id 1 for calling a global function with example usage: test_get_pareto_front_info_infer_n_targets() without return types