code: def test_gamma_exponential(self):
      x_data = np.array([0.1, 0.5, 3.3, 2.7])
  
      alpha0 = 0.5
      beta0 = 1.75
      lam = rvs.Gamma(alpha0, beta0)
      x = rvs.Exponential(lam, sample_shape=4)
  
      lam_cond = ed.complete_conditional(lam, [lam, x])
  
      self.assertIsInstance(lam_cond, rvs.Gamma)
  
      with self.test_session() as sess:
          alpha_val, beta_val = sess.run(
              [lam_cond.concentration, lam_cond.rate], {x: x_data}
          )
  
      self.assertAllClose(alpha_val, alpha0 + len(x_data))
      self.assertAllClose(beta_val, beta0 + 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 7 for calling the test_conjugacy_class obj's underlying member method code with example usage: obj.test_gamma_exponential() without return types