class_docstr: Abstract class for the acquisition maximization.
  
  In order to use this class it has to be subclassed and the
  method `_maximize` must be implemented.
  
  Parameters
  ----------
  configspace : ConfigurationSpace
  acquisition_function : AbstractAcquisitionFunction
  challengers : int, defaults to 5000
      Number of configurations to sample from the configuration space to get
      the acquisition function value for, thus challenging the current
      incumbent and becoming a candidate for the next function evaluation.
  seed : int, defaults to 0
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docstr: Implement acquisition function maximization.
  
  In contrast to `maximize`, this method returns an iterable of tuples, consisting of the acquisition function
  value and the configuration. This allows to plug together different acquisition function maximizers.
  
  Parameters
  ----------
  previous_configs: list[Configuration]
      Previously evaluated configurations.
  n_points: int
      Number of points to be sampled.
  
  Returns
  -------
  challengers : list[tuple[float, Configuration]]
      A list consisting of tuples of acquisition_value and its configuration.
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code: def _maximize(
      self,
      previous_configs: list[Configuration],
      n_points: int,
  ) -> list[tuple[float, Configuration]]:
      """Implement acquisition function maximization.
  
      In contrast to `maximize`, this method returns an iterable of tuples, consisting of the acquisition function
      value and the configuration. This allows to plug together different acquisition function maximizers.
  
      Parameters
      ----------
      previous_configs: list[Configuration]
          Previously evaluated configurations.
      n_points: int
          Number of points to be sampled.
  
      Returns
      -------
      challengers : list[tuple[float, Configuration]]
          A list consisting of tuples of acquisition_value and its configuration.
      """
      raise NotImplementedError()
  
file_path: SMAC3/smac/acquisition/maximizer/abstract_acqusition_maximizer.py
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Any, Iterator
  import numpy
  from ConfigSpace import Configuration, ConfigurationSpace
  from smac.acquisition.function.abstract_acquisition_function import AbstractAcquisitionFunction
  from smac.acquisition.maximizer.helpers import ChallengerList
  from smac.random_design.abstract_random_design import AbstractRandomDesign
  from smac.utils.logging import get_logger
label_desc: Use image node_id 6 for calling the AbstractAcquisitionMaximizer obj's underlying member method code with example usage: obj._maximize(previous_configs, n_points) without return types