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.
docstr: Get value, configuration, and array representation of the *best* configuration.
  
  The definition of best varies depending on the argument ``predict``. If set to `True`,
  this function will return the stats of the best configuration as predicted by the model,
  otherwise it will return the stats for the best observed configuration.
  
  Parameters
  ----------
  predict : bool
      Whether to use the predicted or observed best.
  
  Returns
  -------
  float
  np.ndarry
  Configuration
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code: def _get_x_best(self, X: np.ndarray) -> tuple[np.ndarray, float]:
      """Get value, configuration, and array representation of the *best* configuration.
  
      The definition of best varies depending on the argument ``predict``. If set to `True`,
      this function will return the stats of the best configuration as predicted by the model,
      otherwise it will return the stats for the best observed configuration.
  
      Parameters
      ----------
      predict : bool
          Whether to use the predicted or observed best.
  
      Returns
      -------
      float
      np.ndarry
      Configuration
      """
      if self._predict_x_best:
          model = self._model
          costs = list(
              map(
                  lambda x: (
                      model.predict_marginalized(x.reshape((1, -1)))[0][0][0],  # type: ignore
                      x,
                  ),
                  X,
              )
          )
          costs = sorted(costs, key=lambda t: t[0])
          x_best_array = costs[0][1]
          best_observation = costs[0][0]
  
      # else:
      #    all_configs = self._runhistory.get_configs_per_budget(budget_subset=self._considered_budgets)
      #    x_best = self._incumbent
      #    x_best_array = convert_configurations_to_array(all_configs)
      #    best_observation = self._runhistory.get_cost(x_best)
      #    best_observation_as_array = np.array(best_observation).reshape((1, 1))
  
      #    # It's unclear how to do this for inv scaling and potential future scaling.
      #    # This line should be changed if necessary
      #    best_observation = self._runhistory_encoder.transform_response_values(best_observation_as_array)
      #    best_observation = best_observation[0][0]
  
      return x_best_array, best_observation
  
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 9 for calling the ConfigSelector obj's underlying member method code with example usage: obj._get_x_best(X) and returns: x_best_array,  best_observation