class_docstr: Callable objective function to be used with optuna.
class_docstr_tok: ["Callable","objective","function","to","be","used","with","optuna","."]
code: def join_configs(*configs: dict) -> dict:
      result = {}
      for index, config in enumerate(configs):
          if config is not None:
              result.update(
                  {
                      f"{index}_{key}": value
                      for key, value in config.items()
                  }
              )
  
      return {"algorithm": {"kwargs": result, "classpath": ""}}
  
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 8 for calling the ObjectiveFunction obj's underlying member method code with example usage: obj.join_configs() and returns: dict