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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code: def name(self) -> str:  # noqa: D102
      return f"Integrated Acquisition Function ({self._acquisition_function.__class__.__name__})"
  
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 2 for calling the IntegratedAcquisitionFunction obj's underlying member method code with example usage: obj.name() and returns: str, self, str