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 num_to_bin(self, value, factor=1):
      bin_format = (
          str(0) * (len(self.dims) - len(bin(value)[2:]))
          + bin(value)[2:]
      )
      return [int(i) * factor for i in bin_format]
  
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 2 for calling the DropletTuner obj's underlying member method code with example usage: obj.num_to_bin(value, factor) and returns: unknown