class_docstr: Class for the batch method on 'Batch Bayesian optimization via local penalization' (Gonzalez et al., 2016).
  
  :param acquisition: acquisition function to be used to compute the batch.
  :param batch size: the number of elements in the batch.
code: def __init__(self, acquisition, batch_size):
      super(LocalPenalization, self).__init__(acquisition, batch_size)
      self.acquisition = acquisition
      self.batch_size = batch_size
  
file_path: GPyOpt/GPyOpt/core/evaluators/batch_local_penalization.py
imports: from .base import EvaluatorBase
  import scipy
  from ...util.general import samples_multidimensional_uniform
  import numpy
label_desc: Use image node_id 1 to create a new LocalPenalization object from inherited base classes: EvaluatorBase with example: obj = LocalPenalization(acquisition, batch_size)