class_docstr: Class for a Thompson batch method. Elements are selected iteratively using the current acquistion function but exploring the models
  by using Thompson sampling
  
  :param acquisition: acquisition function to be used to compute the batch.
  :param batch size: the number of elements in the batch.
class_docstr_tok: ["Class","for","a","Thompson","batch","method",".","Elements","are","selected","iteratively","using","the","current","acquistion","function","but","exploring","the","models","by","using","Thompson","sampling",":","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(ThompsonBatch, self).__init__(acquisition, batch_size)
      self.model = self.acquisition.model
      self.optimizer_name = "lbfgs"
      self.f = self.acquisition.acquisition_function
      self.f_df = self.acquisition.acquisition_function_withGradients
      self.space = self.acquisition.space
  
file_path: GPyOpt/GPyOpt/core/evaluators/batch_thompson.py
imports: from .base import SamplingBasedBatchEvaluator
  from ...optimization.anchor_points_generator import ThompsonSamplingAnchorPointsGenerator
  from ...optimization.optimizer import OptLbfgs, apply_optimizer, choose_optimizer
  import numpy
label_desc: Use image node_id 1 to create a new ThompsonBatch object from inherited base classes: SamplingBasedBatchEvaluator with example: obj = ThompsonBatch(acquisition, batch_size)