code: def generative_network(z):
      """Generative network to parameterize generative model. It takes
      latent variables as input and outputs the likelihood parameters.
  
      logits = neural_network(z)
      """
      net = tf.layers.dense(z, 28 * 28, activation=None)
      net = tf.reshape(net, [FLAGS.N, -1])
      return net
  
file_path: edward/examples/factor_analysis.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import os
  import tensorflow
  from edward.models import Bernoulli, Empirical, Normal
  from observations import mnist
  from scipy.misc import imsave
label_desc: Use image node_id 1 for calling a global function with example usage: generative_network(z) and returns: net