class_docstr: Tuner with droplet algorithm.
  
  Parameters
  ----------
  start_position: list of int
      position initial of the space, the default is [0, 0, ..., 0]
  pvalue: float
      statistical value to confidence level, the default is 0.05
class_docstr_tok: ["Tuner","with","droplet","algorithm",".","Parameters","--","--","--","--","--","start_position",":","list","of","int","position","initial","of","the","space",",","the","default","is","[","0",",","0",",","...",",","0","]","pvalue",":","float","statistical","value","to","confidence","level",",","the","default","is","0.05"]
code: def load_history(self, data_set, min_seed_records=500):
      pass
  
file_path: tvm/python/tvm/autotvm/tuner/droplet_tuner.py
imports: import logging
  import os
  import numpy
  from scipy import stats
  from .tuner import Tuner
label_desc: Use image node_id 10 for calling the DropletTuner obj's underlying member method code with example usage: obj.load_history(data_set, min_seed_records) without return types