class_docstr: Runner of Bayesian optimization loop. This class wraps the optimization loop around the different handlers.
  :param model: GPyOpt model class.
  :param space: GPyOpt space class.
  :param objective: GPyOpt objective class.
  :param acquisition: GPyOpt acquisition class.
  :param evaluator: GPyOpt evaluator class.
  :param X_init: 2d numpy array containing the initial inputs (one per row) of the model.
  :param Y_init: 2d numpy array containing the initial outputs (one per row) of the model.
  :param cost: GPyOpt cost class (default, none).
  :param normalize_Y: whether to normalize the outputs before performing any optimization (default, True).
  :param model_update_interval: interval of collected observations after which the model is updated (default, 1).
  :param de_duplication: GPyOpt DuplicateManager class. Avoids re-evaluating the objective at previous, pending or infeasible locations (default, False).
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docstr: Computes the location of the new evaluation (optimizes the acquisition in the standard case).
  :param pending_zipped_X: matrix of input configurations that are in a pending state (i.e., do not have an evaluation yet).
  :param ignored_zipped_X: matrix of input configurations that the user black-lists, i.e., those configurations will not be suggested again.
  :return:
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code: def _compute_next_evaluations(
      self, pending_zipped_X=None, ignored_zipped_X=None
  ):
      """
      Computes the location of the new evaluation (optimizes the acquisition in the standard case).
      :param pending_zipped_X: matrix of input configurations that are in a pending state (i.e., do not have an evaluation yet).
      :param ignored_zipped_X: matrix of input configurations that the user black-lists, i.e., those configurations will not be suggested again.
      :return:
      """
  
      ## --- Update the context if any
      self.acquisition.optimizer.context_manager = ContextManager(
          self.space, self.context
      )
  
      ### --- Activate de_duplication
      if self.de_duplication:
          duplicate_manager = DuplicateManager(
              space=self.space,
              zipped_X=self.X,
              pending_zipped_X=pending_zipped_X,
              ignored_zipped_X=ignored_zipped_X,
          )
      else:
          duplicate_manager = None
  
      ### We zip the value in case there are categorical variables
      return self.space.zip_inputs(
          self.evaluator.compute_batch(
              duplicate_manager=duplicate_manager,
              context_manager=self.acquisition.optimizer.context_manager,
          )
      )
  
imports: import GPyOpt
  import collections
  import numpy
  import time
  import csv
  import logging
  from ..util.general import best_value, normalize
  from ..util.duplicate_manager import DuplicateManager
  from ..core.errors import InvalidConfigError
  from ..core.task.cost import CostModel
  from ..optimization.acquisition_optimizer import ContextManager
label_desc: Use image node_id 8 for calling the BO obj's underlying member method code with example usage: obj._compute_next_evaluations(pending_zipped_X, ignored_zipped_X) and returns: self