class_docstr: CEMExplainer can be used to compute contrastive explanations for image and tabular data.
  This is achieved by finding what is minimally sufficient (PP - Pertinent Positive) and
  what should be necessarily absent (PN - Pertinent Negative) to maintain the original classification.
  We use elastic norm regularization to ensure minimality for both parts of the explanation
  i.e. PPs and PNs. An autoencoder can optionally be used to make the explanations more realistic. [#]_
  
  References:
      .. [#] `Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu,
         Paishun Ting, Karthikeyan Shanmugam, Payel Das, "Explanations based on
         the Missing: Towards Contrastive Explanations with Pertinent Negatives,"
         Advances in Neural Information Processing Systems (NeurIPS), 2018.
         <https://arxiv.org/abs/1802.07623>`_
docstr: Set parameters for the explainer.
code: def set_params(self, *argv, **kwargs):
      """
      Set parameters for the explainer.
      """
      pass
  
file_path: AIX360/aix360/algorithms/contrastive/CEM.py
imports: from __future__ import print_function
  from aix360.algorithms.lwbe import LocalWBExplainer
  from .CEM_aen import AEADEN
  import random
  import numpy
label_desc: Use image node_id 2 for calling the CEMExplainer obj's underlying member method code with example usage: obj.set_params() without return types