code: def test_normal_normal(self):
      x_data = np.array([0.1, 0.5, 3.3, 2.7])
  
      mu0 = 0.3
      sigma0 = 2.1
      sigma_likelihood = 1.2
  
      mu = rvs.Normal(mu0, sigma0)
      x = rvs.Normal(mu, sigma_likelihood, sample_shape=len(x_data))
  
      mu_cond = ed.complete_conditional(mu, [mu, x])
      self.assertIsInstance(mu_cond, rvs.Normal)
  
      with self.test_session() as sess:
          mu_val, sigma_val = sess.run(
              [mu_cond.loc, mu_cond.scale], {x: x_data}
          )
  
      self.assertAllClose(
          sigma_val,
          (1.0 / sigma0**2 + len(x_data) / sigma_likelihood**2)
          ** -0.5,
      )
      self.assertAllClose(
          mu_val,
          sigma_val**2
          * (
              mu0 / sigma0**2
              + (1.0 / sigma_likelihood**2 * x_data.sum())
          ),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import numpy
  import tensorflow
  from edward import models
label_desc: Use image node_id 11 for calling the test_conjugacy_class obj's underlying member method code with example usage: obj.test_normal_normal() without return types