class_docstr: Jointly trains 2 surrogate decision trees.
  Refines diff (or sim) regions by sample generation.
code: def split_condition(
      self,
      col1,
      cutoff1,
      entropy1,
      col2,
      cutoff2,
      entropy2,
      col,
      cutoff,
      entropy,
  ):
      if entropy1 <= 0.0:  # floating point/1e-8 error, -1e-8 is zero
          return False
      if entropy2 <= 0.0:
          return False
      return True
  
imports: import numpy
  import pandas
  from aix360.algorithms.imd.rule import Rule
label_desc: Use image node_id 9 for calling the JointSurrogateTree obj's underlying member method code with example usage: obj.split_condition(col1, cutoff1, entropy1, col2, cutoff2, entropy2, col, cutoff, entropy) and returns: True, False, False