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 decode(self, z):
      if self.output_activation_type is None:
          return self.generative_net(z)
      elif self.output_activation_type == "sigmoid":
          return torch.sigmoid(self.generative_net(z))
      elif self.output_activation_type == "tanh":
          return torch.tanh(self.generative_net(z))
      else:
          raise NotImplementedError(
              "Unsupported output activation type."
          )
  
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 3 for calling the DIPVAE obj's underlying member method code with example usage: obj.decode(z) and returns: self, torch, torch