class_docstr: The config selector handles the surrogate model and the acquisition function. Based on both components, the next
  configuration is selected.
  
  Parameters
  ----------
  retrain_after : int, defaults to 8
      How many configurations should be returned before the surrogate model is retrained.
  retries : int, defaults to 8
      How often to retry receiving a new configuration before giving up.
  min_trials: int, defaults to 1
      How many samples are required to train the surrogate model. If budgets are involved,
      the highest budgets are checked first. For example, if min_trials is three, but we find only
      two trials in the runhistory for the highest budget, we will use trials of a lower budget
      instead.
code: def _get_evaluated_configs(self) -> list[Configuration]:
      assert self._runhistory is not None
      return self._runhistory.get_configs_per_budget(
          budget_subset=self._considered_budgets
      )
  
imports: from __future__ import annotations
  from typing import Any, Iterator
  import copy
  import numpy
  from ConfigSpace import Configuration
  from smac.acquisition.function.abstract_acquisition_function import AbstractAcquisitionFunction
  from smac.acquisition.maximizer.abstract_acqusition_maximizer import AbstractAcquisitionMaximizer
  from smac.callback.callback import Callback
  from smac.initial_design import AbstractInitialDesign
  from smac.model.abstract_model import AbstractModel
  from smac.random_design.abstract_random_design import AbstractRandomDesign
  from smac.runhistory.encoder.abstract_encoder import AbstractRunHistoryEncoder
  from smac.runhistory.runhistory import RunHistory
  from smac.scenario import Scenario
  from smac.utils.logging import get_logger
label_desc: Use image node_id 8 for calling the ConfigSelector obj's underlying member method code with example usage: obj._get_evaluated_configs() and returns: self