class_docstr: Callable objective function to be used with optuna.
class_docstr_tok: ["Callable","objective","function","to","be","used","with","optuna","."]
code: def separate_configs(configs: dict) -> tuple[dict]:
      results = []
      max_iter = max([int(key.split("_")[0]) for key in configs.keys()])
      max_iter = 2 if max_iter < 2 else max_iter
  
      for index in range(max_iter + 1):
          temp_dict = {}
          for key, value in configs.items():
              if key.startswith(str(index) + "_"):
                  temp_dict.update(
                      {"_".join(key.split("_")[1:]): value}
                  )
          results.append(temp_dict)
  
      return tuple(results)
  
imports: import dataclasses
  import logging
  import pickle
  import time
  from pathlib import Path
  from typing import Optional, Union
  import numpy
  import pandas
  from hpt.suggest import suggest_callable_hyperparams
  from omegaconf import DictConfig, OmegaConf
  from optuna.trial import BaseTrial
  from ..evaluation import evaluate_fairness, evaluate_performance
  from ..methods import InProcessing, PostProcessing, PreProcessing
  from ..methods.postprocessing.threshold import Threshold
label_desc: Use image node_id 9 for calling the ObjectiveFunction obj's underlying member method code with example usage: obj.separate_configs(configs) and returns: tuple