class_docstr: Implement SMAC's default acquisition function optimization.
  
  This optimizer performs local search from the previous best points according, to the acquisition
  function, uses the acquisition function to sort randomly sampled configurations.
  Random configurations are interleaved by the main SMAC code.
  
  The Random configurations are interleaved to circumvent issues from a constant prediction
  from the Random Forest model at the beginning of the optimization process.
  
  Parameters
  ----------
  configspace : ConfigurationSpace
  acquisition_function : AbstractAcquisitionFunction | None, defaults to None
  challengers : int, defaults to 5000
      Number of challengers.
  max_steps: int | None, defaults to None
      [LocalSearch] Maximum number of steps that the local search will perform.
  n_steps_plateau_walk: int, defaults to 10
      [LocalSearch] number of steps during a plateau walk before local search terminates.
  local_search_iterations: int, defauts to 10
      [Local Search] number of local search iterations.
  seed : int, defaults to 0
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code: def meta(self) -> dict[str, Any]:  # noqa: D102
      meta = super().meta
      meta.update(
          {
              "random_search": self._random_search.meta,
              "local_search": self._local_search.meta,
          }
      )
  
      return meta
  
file_path: SMAC3/smac/acquisition/maximizer/local_and_random_search.py
imports: from __future__ import annotations
  from typing import Any
  from ConfigSpace import Configuration, ConfigurationSpace
  from smac.acquisition.function import AbstractAcquisitionFunction
  from smac.acquisition.maximizer.abstract_acqusition_maximizer import AbstractAcquisitionMaximizer
  from smac.acquisition.maximizer.local_search import LocalSearch
  from smac.acquisition.maximizer.random_search import RandomSearch
  from smac.utils.logging import get_logger
label_desc: Use image node_id 4 for calling the LocalAndSortedRandomSearch obj's underlying member method code with example usage: obj.meta() and returns: meta