class_docstr: ProtodashExplainer provides exemplar-based explanations for summarizing datasets as well
  as explaining predictions made by an AI model. It employs a fast gradient based algorithm
  to find prototypes along with their (non-negative) importance weights. The algorithm minimizes the maximum
  mean discrepancy metric and has constant factor approximation guarantees for this weakly submodular function. [#]_.
  
  References:
      .. [#] `Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo Cecchi,
         "ProtoDash: Fast Interpretable Prototype Selection"
         <https://arxiv.org/abs/1707.01212>`_
docstr: Set parameters for the explainer.
docstr_tok: ["Set","parameters","for","the","explainer","."]
code: def set_params(self, *argv, **kwargs):
      """
      Set parameters for the explainer.
      """
      pass
  
imports: from __future__ import print_function
  from die import DIExplainer
  from PDASH_utils import HeuristicSetSelection
label_desc: Use image node_id 2 for calling the ProtodashExplainer obj's underlying member method code with example usage: obj.set_params() without return types