class_docstr: Disentangled Inferred Prior-VAE or DIPVAE [1] 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
code: def _get_dipvae_regularizer(self, cov_z, lambda_offdiag, lambda_diag):
      cov_z_diag = torch.diag(cov_z)
      cov_z_offdiag = cov_z - torch.diag(cov_z_diag)
  
      return lambda_offdiag * torch.sum(
          cov_z_offdiag**2
      ) + lambda_diag * torch.sum((cov_z_diag - 1) ** 2)
  
imports: import matplotlib
  import matplotlib.pyplot
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  from torch.nn import Linear
  import os
label_desc: Use image node_id 7 for calling the DIPVAE obj's underlying member method code with example usage: obj._get_dipvae_regularizer(cov_z, lambda_offdiag, lambda_diag) and returns: unknown