class_docstr: A general interface for multi-objective optimizer, depending on different strategies.
docstr: Transform a multi-objective loss to a single loss.
  
  Parameters
  ----------
  values : list[float]
      Normalized values in the range [0, 1].
  
  Returns
  -------
  cost : float
      Combined cost.
docstr_tok: ["Transform","a","multi-objective","loss","to","a","single","loss",".","Parameters","--","--","--","--","--","values",":","list","[","float","]","Normalized","values","in","the","range","[","0",",","1","]",".","Returns","--","--","--","-","cost",":","float","Combined","cost","."]
code: def __call__(self, values: list[float]) -> float:
      """Transform a multi-objective loss to a single loss.
  
      Parameters
      ----------
      values : list[float]
          Normalized values in the range [0, 1].
  
      Returns
      -------
      cost : float
          Combined cost.
      """
      raise NotImplementedError
  
file_path: SMAC3/smac/multi_objective/abstract_multi_objective_algorithm.py
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Any
label_desc: Use image node_id 4 for calling the AbstractMultiObjectiveAlgorithm obj's underlying member method code with example usage: obj.__call__(values) without return types