code: def _multiobjective_func(
      trial: optuna.trial.Trial,
  ) -> Tuple[float, float]:
      x = trial.suggest_float("x", low=-1.0, high=1.0)
      y = trial.suggest_float("y", low=20, high=30, log=True)
      z = trial.suggest_categorical("z", (-1.0, 1.0))
      first_objective = (x - 2) ** 2 + (y - 25) ** 2 + z
      second_objective = (x - 2) ** 3 + (y - 25) ** 3 - z
  
      return first_objective, second_objective
  
imports: from typing import Callable
  from typing import List
  from typing import Optional
  from typing import Tuple
  from typing import Union
  import numpy
  import py
  import pytest
  import optuna
  from optuna._imports import try_import
  from optuna.integration.mlflow import MLflowCallback
label_desc: Use image node_id 2 for calling a global function with example usage: _multiobjective_func(trial) and returns: first_objective,  second_objective