code: def testLogNormalizerIsNotInf(self):
      # Parameterize by logits large in magnitude.
      logits = np.array(
          [-1000.0, -100.0, 100.0, 1000.0], dtype=np.float32
      )
      dist = continuous_bernoulli.ContinuousBernoulli(logits=logits)
      log_normalizer_ = self.evaluate(dist._log_normalizer())
      self.assertFalse(np.any(np.isinf(log_normalizer_)))
      # The normalizer grows roughly log(abs(logits))
      self.assertAllClose(log_normalizer_, np.log(np.abs(logits)))
  
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 11 for calling the ContinuousBernoulliTest obj's underlying member method code with example usage: obj.testLogNormalizerIsNotInf() without return types