class_docstr: This class handles specific types of batch evaluators, based on the sampling of anchor points (examples are random and Thompson sampling).
docstr: convenient helper
  :param x: input configuration in the model space
  :return: zipped x as a tuple
docstr_tok: ["convenient","helper",":","param","x",":","input","configuration","in","the","model","space",":","return",":","zipped","x","as","a","tuple"]
code: def zip_and_tuple(self, x):
      """
      convenient helper
      :param x: input configuration in the model space
      :return: zipped x as a tuple
      """
      return tuple(self.space.zip_inputs(np.atleast_2d(x)).flatten())
  
file_path: GPyOpt/GPyOpt/core/evaluators/base.py
imports: import numpy
label_desc: Use image node_id 7 for calling the SamplingBasedBatchEvaluator obj's underlying member method code with example usage: obj.zip_and_tuple(x) and returns: tuple