class_docstr: DISExplainer is the base class for Directly Interpretable Supervised Explainers (DISE).
  Such explainers attempt to train explainable models directly from data.
  Examples include Boolean Decision Rules via Column Generation[#1]_, Generalized Linear Rule Models[#2]_,
  TED: Teaching AI to Explain its Decisions[#3]_, etc.
  
  References:
      .. [#1] Boolean Decision Rules via Column Generation, NIPS 2018.
      Dash, Gunluk, Wei. https://arxiv.org/abs/1805.09901
      .. [#2] Generalized Linear Rule Models, ICML 2019.
      Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Gunluk.
      https://arxiv.org/abs/1906.01761
      .. [#3] TED: Teaching AI to Explain its Decisions, AIES 2019.
      Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar,
      Aleksandra Mojsilovic, Karthikeyan Natesan Ramamurthy, Kush R. Varshney.
      https://arxiv.org/abs/1811.04896
docstr: Fit a model on data.
code: def fit(self, *argv, **kwargs):
      """
      Fit a model on data.
      """
      raise NotImplementedError
  
file_path: AIX360/aix360/algorithms/dise.py
imports: import abc
  import sys
label_desc: Use image node_id 4 for calling the DISExplainer obj's underlying member method code with example usage: obj.fit() without return types