class_docstr: DIPVAEExplainer can be used to visualize the changes in the latent space of Disentangled Inferred Prior-VAE
  or DIPVAE [#1]_. This model is a Variational Autoencoder [#2]_ variant that leads to a
  disentangled latent space. This is achieved by matching the covariance of the prior distributions with the
  inferred prior.
  
  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] `Diederik P Kingma and Max Welling. Auto-encoding variational Bayes. ICLR, 2014. <https://arxiv.org/pdf/1312.6114.pdf>`_
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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.
      """
      print("TBD: Implement set params in DIPVAEExplainer")
  
file_path: AIX360/aix360/algorithms/dipvae/dipvae.py
imports: from __future__ import print_function
  import torch
  from torch import __init__
  from torch.optim import Adam
  from .dipvae_utils import VAE, DIPVAE
  from .dipvae_utils import plot_reconstructions, plot_latent_traversal
  from aix360.algorithms.die import DIExplainer
  import os
  import sys
  import random
  import time
  import numpy
label_desc: Use image node_id 2 for calling the DIPVAEExplainer obj's underlying member method code with example usage: obj.set_params() without return types