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 forward(self, x, return_z_sample=False):
      z_sample, mu_z, std_z = self.encode(x)
      if return_z_sample:
          return self.decode(z_sample), mu_z, std_z, z_sample
      else:
          return self.decode(z_sample), mu_z, std_z
  
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 4 for calling the DIPVAE obj's underlying member method code with example usage: obj.forward(x, return_z_sample) and returns: self,  mu_z,  std_z,  z_sample, self,  mu_z,  std_z