class_docstr: DIExplainer is the base class for Directly Interpretable unsupervised explainers (DIE).
  Such explainers generally rely on unsupervised techniques to explain datasets and model predictions.
  Examples include DIP-VAE[#1]_, Protodash[#2]_, etc.
  
  References:
      .. [#1] Variational Inference of Disentangled Latent Concepts from Unlabeled Observations (DIP-VAE), ICLR 2018.
       Kumar, Sattigeri, Balakrishnan. https://arxiv.org/abs/1711.00848
      .. [#2] ProtoDash: Fast Interpretable Prototype Selection, 2017.
      Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo Cecchi.
      https://arxiv.org/abs/1707.01212
docstr: Explain the data or model.
docstr_tok: ["Explain","the","data","or","model","."]
code: def explain(self, *argv, **kwargs):
      """
      Explain the data or model.
      """
      raise NotImplementedError
  
imports: import abc
  import sys
label_desc: Use image node_id 3 for calling the DIExplainer obj's underlying member method code with example usage: obj.explain() without return types