class_docstr: Compute the integrated acquisition function by marginalizing over model hyperparameters
  
  See "Practical Bayesian Optimization of Machine Learning Algorithms" by Jasper Snoek et al.
  (https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf)
  for further details.
  
  Parameters
  ----------
  acquisition_function : AbstractAcquisitionFunction
      Acquisition function to be integrated.
  
  Attributes
  ----------
  _acquisition_function : AbstractAcquisitionFunction
      Acquisition function to be integrated.
  _functions: list[AbstractAcquisitionFunction]
      Holds n (n = number of models) copies of the acquisition function.
  _eta : float
      Current incumbent function value.
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docstr: Compute integrated acquisition values
  
  Parameters
  ----------
  X : np.ndarray [N, D]
      The input points where the acquisition function should be evaluated. The dimensionality of X is (N, D),
      with N as the number of points to evaluate at and D is the number of dimensions of one X.
  
  Returns
  -------
  np.ndarray [N,1]
      Acquisition function values wrt X.
  
  Raises
  ------
  ValueError
      If `update` has not been called first (`_functions` not up to date).
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code: def _compute(self, X: np.ndarray) -> np.ndarray:
      """Compute integrated acquisition values
  
      Parameters
      ----------
      X : np.ndarray [N, D]
          The input points where the acquisition function should be evaluated. The dimensionality of X is (N, D),
          with N as the number of points to evaluate at and D is the number of dimensions of one X.
  
      Returns
      -------
      np.ndarray [N,1]
          Acquisition function values wrt X.
  
      Raises
      ------
      ValueError
          If `update` has not been called first (`_functions` not up to date).
      """
      if self._functions is None:
          raise ValueError("Need to call `update` first!")
  
      return np.array(
          [func._compute(X) for func in self._functions]
      ).mean(axis=0)
  
file_path: SMAC3/smac/acquisition/function/integrated_acquisition_function.py
imports: from __future__ import annotations
  from typing import Any
  import copy
  import numpy
  from smac.acquisition.function.abstract_acquisition_function import AbstractAcquisitionFunction
  from smac.model.abstract_model import AbstractModel
  from smac.utils.logging import get_logger
label_desc: Use image node_id 5 for calling the IntegratedAcquisitionFunction obj's underlying member method code with example usage: obj._compute(X) and returns: np