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 p_value(self, elem_1, elem_2):
      if len(elem_1) <= 1 or len(elem_2) <= 1:
          return True
      return (
          stats.ttest_ind(np.array(elem_1), np.array(elem_2)).pvalue
          <= self.pvalue
      )
  
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 5 for calling the DropletTuner obj's underlying member method code with example usage: obj.p_value(elem_1, elem_2) and returns: unknown, True