class_docstr: ParEGO implementation based on https://www.cs.bham.ac.uk/~jdk/UKCI-2015.pdf.
  
  Parameters
  ----------
  scenario : Scenario
  rho : float, defaults to 0.05
      A small positive value.
  seed : int | None, defaults to None
class_docstr_tok: ["ParEGO","implementation","based","on","https",":","\/\/www.cs.bham.ac.uk\/~jdk\/UKCI-2015.pdf",".","Parameters","--","--","--","--","--","scenario",":","Scenario","rho",":","float",",","defaults","to","0.05","A","small","positive","value",".","seed",":","int","|","None",",","defaults","to","None"]
code: def __call__(self, values: list[float]) -> float:  # noqa: D102
      # Weight the values
      if self._theta is None:
          raise ValueError(
              "Iteration not yet initalized; Call `update_on_iteration_start()` first"
          )
  
      theta_f = self._theta * values
      return float(
          np.max(theta_f, axis=0) + self._rho * np.sum(theta_f, axis=0)
      )
  
file_path: SMAC3/smac/multi_objective/parego.py
imports: from __future__ import annotations
  from typing import Any
  import numpy
  from smac.multi_objective.abstract_multi_objective_algorithm import AbstractMultiObjectiveAlgorithm
  from smac.scenario import Scenario
label_desc: Use image node_id 4 for calling the ParEGO obj's underlying member method code with example usage: obj.__call__(values) and returns: float