class_docstr: Class for Local Penalization acquisition. Used for batch design.
  :param model: model of the class GPyOpt
  :param space: design space of the class GPyOpt.
  :param optimizer: optimizer of the class GPyOpt.
  :param acquisition: acquisition function of the class GPyOpt
  :param transform: transformation applied to the acquisition (default, none).
  
  .. Note:: irrespective of the transformation applied the penalized acquisition is always mapped again to the log space.
  This way gradients can be computed additively and are more stable.
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docstr: Pre-computes the parameters of a penalizer centered at x0.
docstr_tok: ["Pre-computes","the","parameters","of","a","penalizer","centered","at","x0","."]
code: def _hammer_function_precompute(self, x0, L, Min, model):
      """
      Pre-computes the parameters of a penalizer centered at x0.
      """
      if x0 is None:
          return None, None
      if len(x0.shape) == 1:
          x0 = x0[None, :]
      m = model.predict(x0)[0]
      pred = model.predict(x0)[1].copy()
      pred[pred < 1e-16] = 1e-16
      s = np.sqrt(pred)
      r_x0 = (m - Min) / L
      s_x0 = s / L
      r_x0 = r_x0.flatten()
      s_x0 = s_x0.flatten()
      return r_x0, s_x0
  
file_path: GPyOpt/GPyOpt/acquisitions/LP.py
imports: from .base import AcquisitionBase
  from .LCB import AcquisitionLCB
  from .LCB_mcmc import AcquisitionLCB_MCMC
  import numpy
  from scipy.stats import norm
label_desc: Use image node_id 3 for calling the AcquisitionLP obj's underlying member method code with example usage: obj._hammer_function_precompute(x0, L, Min, model) and returns: r_x0,  s_x0, None,  None