class_docstr: Weight the acquisition function with a user-defined prior over the optimum.
  
  See "piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization" by Carl
  Hvarfner et al. [HSSL22]_ for further details.
  
  Parameters
  ----------
  decay_beta: float
      Decay factor on the user prior. A solid default value for decay_beta (empirically founded) is
      ``scenario.n_trials`` / 10.
  prior_floor : float, defaults to 1e-12
      Lowest possible value of the prior to ensure non-negativity for all values in the search space.
  discretize : bool, defaults to False
      Whether to discretize (bin) the densities for continous parameters. Triggered for Random Forest models and
      continous hyperparameters to avoid a pathological case where all Random Forest randomness is removed
      (RF surrogates require piecewise constant acquisition functions to be well-behaved).
  discrete_bins_factor : float, defaults to 10.0
      If discretizing, the multiple on the number of allowed bins for each parameter.
code: def meta(self) -> dict[str, Any]:  # noqa: D102
      meta = super().meta
      meta.update(
          {
              "acquisition_function": self._acquisition_function.meta,
              "decay_beta": self._decay_beta,
              "prior_floor": self._prior_floor,
              "discretize": self._discretize,
              "discrete_bins_factor": self._discrete_bins_factor,
          }
      )
  
      return meta
  
imports: from __future__ import annotations
  from typing import Any
  import numpy
  from ConfigSpace import Configuration
  from ConfigSpace.hyperparameters import FloatHyperparameter
  from smac.acquisition.function.abstract_acquisition_function import AbstractAcquisitionFunction
  from smac.acquisition.function.confidence_bound import LCB
  from smac.acquisition.function.integrated_acquisition_function import IntegratedAcquisitionFunction
  from smac.acquisition.function.thompson import TS
  from smac.model.abstract_model import AbstractModel
  from smac.model.random_forest.abstract_random_forest import AbstractRandomForest
  from smac.utils.logging import get_logger
label_desc: Use image node_id 3 for calling the PriorAcquisitionFunction obj's underlying member method code with example usage: obj.meta() and returns: meta