code: def test_normal_run(self):
      def ratio_estimator(data, local_vars, global_vars):
          """Use the optimal ratio estimator, r(z) = log p(z). We add a
          TensorFlow variable as the algorithm assumes that the function
          has parameters to optimize."""
          w = tf.get_variable("w", [])
          return z.log_prob(local_vars[z]) + w
  
      with self.test_session() as sess:
          z = Normal(loc=5.0, scale=1.0)
  
          qz = Normal(
              loc=tf.Variable(tf.random_normal([])),
              scale=tf.nn.softplus(tf.Variable(tf.random_normal([]))),
          )
  
          inference = ed.ImplicitKLqp(
              {z: qz}, discriminator=ratio_estimator
          )
          inference.run(n_iter=200)
  
          self.assertAllClose(qz.mean().eval(), 5.0, atol=1.0)
  
file_path: edward/tests/inferences/implicitklqp_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import tensorflow
  from edward.models import Normal
label_desc: Use image node_id 1 for calling the test_implicit_klqp_class obj's underlying member method code with example usage: obj.test_normal_run() and returns: unknown