class_docstr: An object to compute the acquisition functions.
  
  kind: {'ucb', 'ei', 'poi'}
      * 'ucb' stands for the Upper Confidence Bounds method
      * 'ei' is the Expected Improvement method
      * 'poi' is the Probability Of Improvement criterion.
  
  kappa: float, optional(default=2.576)
          Parameter to indicate how closed are the next parameters sampled.
          Higher value = favors spaces that are least explored.
          Lower value = favors spaces where the regression function is
          the highest.
  
  kappa_decay: float, optional(default=1)
      `kappa` is multiplied by this factor every iteration.
  
  kappa_decay_delay: int, optional(default=0)
      Number of iterations that must have passed before applying the
      decay to `kappa`.
  
  xi: float, optional(default=0.0)
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code: def update_params(self):
      self._iters_counter += 1
  
      if (
          self._kappa_decay < 1
          and self._iters_counter > self._kappa_decay_delay
      ):
          self.kappa *= self._kappa_decay
  
imports: import warnings
  import numpy
  from scipy.stats import norm
  from scipy.optimize import minimize
  from colorama import just_fix_windows_console
  import json
label_desc: Use image node_id 2 for calling the UtilityFunction obj's underlying member method code with example usage: obj.update_params() without return types