class_docstr: Abstract algorithm that generates new set of parameters.
class_docstr_tok: ["Abstract","algorithm","that","generates","new","set","of","parameters","."]
docstr: Reinstantiates the algorithm when loaded.
  
  Args:
      num_trials (int): number of trials in study so far.
docstr_tok: ["Reinstantiates","the","algorithm","when","loaded",".","Args",":","num_trials","(","int",")",":","number","of","trials","in","study","so","far","."]
code: def load(self, num_trials):
      """
      Reinstantiates the algorithm when loaded.
  
      Args:
          num_trials (int): number of trials in study so far.
      """
      pass
  
imports: import os
  import random
  import numpy
  import logging
  import sherpa
  import pandas
  import sklearn.gaussian_process
  from sherpa.core import Choice, Continuous, Discrete, Ordinal, AlgorithmState
  from sherpa.core import rng
  import sklearn.model_selection
  import collections
label_desc: Use image node_id 2 for calling the Algorithm obj's underlying member method code with example usage: obj.load(num_trials) without return types