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","."]
code: def __init__(self, acquisition, batch_size=1):
      super(Sequential, self).__init__(acquisition, batch_size)
  
imports: from .base import EvaluatorBase
label_desc: Use image node_id 1 to create a new Sequential object from inherited base classes: EvaluatorBase with example: obj = Sequential(acquisition, batch_size)