code: def testProbitBernoulliProbitBernoulliKL(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 = probit_bernoulli.ProbitBernoulli(
          probs=a_p, validate_args=True
      )
      b = probit_bernoulli.ProbitBernoulli(
          probs=b_p, validate_args=True
      )
  
      kl = kullback_leibler.kl_divergence(a, b)
      kl_val = self.evaluate(kl)
  
      kl_expected = a_p * np.log(a_p / b_p) + (1.0 - a_p) * np.log(
          (1.0 - a_p) / (1.0 - 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 kullback_leibler
  from tensorflow_probability.python.distributions import probit_bernoulli
  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 22 for calling the ProbitBernoulliTest obj's underlying member method code with example usage: obj.testProbitBernoulliProbitBernoulliKL() without return types