class_docstr: Class for standard Sequential Bayesian optimization methods.
  
  :param acquisition: acquisition function to be used to compute the batch.
  :param batch size: it is 1 by default since this class is only used for sequential methods.
class_docstr_tok: ["Class","for","standard","Sequential","Bayesian","optimization","methods",".",":","param","acquisition",":","acquisition","function","to","be","used","to","compute","the","batch",".",":","param","batch","size",":","it","is","1","by","default","since","this","class","is","only","used","for","sequential","methods","."]
docstr: Selects the new location to evaluate the objective.
docstr_tok: ["Selects","the","new","location","to","evaluate","the","objective","."]
code: def compute_batch(self, duplicate_manager=None, context_manager=None):
      """
      Selects the new location to evaluate the objective.
      """
      x, _ = self.acquisition.optimize(
          duplicate_manager=duplicate_manager
      )
      return x
  
imports: from .base import EvaluatorBase
label_desc: Use image node_id 2 for calling the Sequential obj's underlying member method code with example usage: obj.compute_batch(duplicate_manager, context_manager) and returns: x