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: Return the meta-data of the created object.
docstr_tok: ["Return","the","meta-data","of","the","created","object","."]
code: def meta(self) -> dict[str, Any]:
      """Return the meta-data of the created object."""
      acquisition_function_meta = None
      if self._acquisition_function is not None:
          acquisition_function_meta = self._acquisition_function.meta
  
      return {
          "name": self.__class__.__name__,
          "acquisition_function": acquisition_function_meta,
          "challengers": self._challengers,
          "seed": self._seed,
      }
  
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 4 for calling the AbstractAcquisitionMaximizer obj's underlying member method code with example usage: obj.meta() and returns: dict