class_docstr: Jointly trains 2 surrogate decision trees.
  Refines diff (or sim) regions by sample generation.
code: def split_condition2(
      self,
      col1,
      cutoff1,
      entropy1,
      col2,
      cutoff2,
      entropy2,
      col,
      cutoff,
      entropy,
  ):
      if (
          entropy1 < 0 or entropy2 < 0 or entropy < 0
      ):  # floating point/1e-8 error
          return True
  
      e_avg = (entropy1 + entropy2) / 2
      alpha_e_joint = self.alpha * entropy
  
      return alpha_e_joint < e_avg
  
imports: import numpy
  import pandas
  from aix360.algorithms.imd.rule import Rule
label_desc: Use image node_id 10 for calling the JointSurrogateTree obj's underlying member method code with example usage: obj.split_condition2(col1, cutoff1, entropy1, col2, cutoff2, entropy2, col, cutoff, entropy) and returns: unknown, True