code: def discriminative_network(x, y):
      # Discriminator must output probability in logits
      net = tf.concat([x, y], 1)
      net = tf.layers.dense(
          net, FLAGS.hidden_units, activation=leakyrelu
      )
      net = tf.layers.dense(net, 1, activation=None)
      return net
  
file_path: edward/examples/bigan.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  import edward
  import matplotlib.pyplot
  import matplotlib.gridspec
  import numpy
  import os
  import tensorflow
  from observations import mnist
label_desc: Use image node_id 5 for calling a global function with example usage: discriminative_network(x, y) and returns: net