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_covariance_mu_z(self, mu_z):
      zero_mean_mu_z = mu_z - torch.mean(mu_z, dim=1, keepdim=True)
      zero_mean_mu_z_t = zero_mean_mu_z.t()
  
      return zero_mean_mu_z_t.matmul(zero_mean_mu_z).squeeze()
  
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 6 for calling the DIPVAE obj's underlying member method code with example usage: obj._get_covariance_mu_z(mu_z) and returns: zero_mean_mu_z_t