code: def testPmfWithFloatArgReturnsXEntropy(self):
      p = [[0.2], [0.4], [0.3], [0.6]]
      samps = [0, 0.1, 0.8]
      self.assertAllClose(
          np.float32(samps) * np.log(np.float32(p))
          + (1 - np.float32(samps)) * np.log(1 - np.float32(p)),
          self.evaluate(
              probit_bernoulli.ProbitBernoulli(
                  probs=p, validate_args=False
              ).log_prob(samps)
          ),
      )
  
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 11 for calling the ProbitBernoulliTest obj's underlying member method code with example usage: obj.testPmfWithFloatArgReturnsXEntropy() without return types