code: def testContinuousBernoulliContinuousBernoulliKL(self):
      batch_size = 6
      a_p = np.array([0.6] * batch_size, dtype=np.float32)
      b_p = np.array([0.4] * batch_size, dtype=np.float32)
  
      a = continuous_bernoulli.ContinuousBernoulli(
          probs=a_p, validate_args=True
      )
      b = continuous_bernoulli.ContinuousBernoulli(
          probs=b_p, validate_args=True
      )
  
      kl = kullback_leibler.kl_divergence(a, b)
      kl_val = self.evaluate(kl)
  
      kl_expected = (
          mean(a_p)
          * (
              np.log(a_p)
              + np.log1p(-b_p)
              - np.log(b_p)
              - np.log1p(-a_p)
          )
          + log_norm_const(a_p)
          - log_norm_const(b_p)
          + np.log1p(-a_p)
          - np.log1p(-b_p)
      )
  
      self.assertEqual(kl.shape, (batch_size,))
      self.assertAllClose(kl_val, kl_expected)
  
imports: import numpy
  from scipy import special
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import continuous_bernoulli
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 33 for calling the ContinuousBernoulliTest obj's underlying member method code with example usage: obj.testContinuousBernoulliContinuousBernoulliKL() without return types