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 sample_from_gen_model(self, num_samples=25):
      self.eval()
      z_samples = torch.randn(num_samples, self.latent_dim)
      if self.cuda_available:
          z_samples = z_samples.cuda()
      self.train()
      return self.decode(z_samples)
  
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 5 for calling the DIPVAE obj's underlying member method code with example usage: obj.sample_from_gen_model(num_samples) and returns: self