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: Constructor method, initializes the explainer
docstr_tok: ["Constructor","method",",","initializes","the","explainer"]
code: def __init__(self):
      """
      Constructor method, initializes the explainer
      """
      super(ProtodashExplainer, self).__init__()
  
imports: from __future__ import print_function
  from aix360.algorithms.die import DIExplainer
  from .PDASH_utils import HeuristicSetSelection
label_desc: Use image node_id 1 to create a new ProtodashExplainer object from inherited base classes: DIExplainer with example: obj = ProtodashExplainer()