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: Creates the function to define the exclusion zones
docstr_tok: ["Creates","the","function","to","define","the","exclusion","zones"]
code: def _hammer_function(self, x, x0, r_x0, s_x0):
      """
      Creates the function to define the exclusion zones
      """
      return norm.logcdf(
          (
              np.sqrt(
                  (
                      np.square(
                          np.atleast_2d(x)[:, None, :]
                          - np.atleast_2d(x0)[None, :, :]
                      )
                  ).sum(-1)
              )
              - 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 4 for calling the AcquisitionLP obj's underlying member method code with example usage: obj._hammer_function(x, x0, r_x0, s_x0) and returns: norm