code: def testKLAgainstCategoricalDistribution(self, batch_size):
      cutpoints = self._random_cutpoints([100])
      a_loc = self._random_location([batch_size])
      b_loc = self._random_location([batch_size])
  
      a = ol.OrderedLogistic(
          cutpoints=cutpoints, loc=a_loc, validate_args=True
      )
      b = ol.OrderedLogistic(
          cutpoints=cutpoints, loc=b_loc, validate_args=True
      )
  
      a_cat = categorical.Categorical(
          logits=a.categorical_log_probs(), validate_args=True
      )
      b_cat = categorical.Categorical(
          logits=b.categorical_log_probs(), validate_args=True
      )
  
      kl = self.evaluate(kullback_leibler.kl_divergence(a, b))
      self.assertEqual(kl.shape, (batch_size,))
  
      kl_expected = self.evaluate(
          kullback_leibler.kl_divergence(a_cat, b_cat)
      )
      self.assertAllClose(kl, kl_expected)
  
      kl_same = self.evaluate(kullback_leibler.kl_divergence(a, a))
      self.assertAllClose(kl_same, np.zeros_like(kl_expected))
  
imports: import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import ascending
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import ordered_logistic
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 10 for calling the OrderedLogisticTest obj's underlying member method code with example usage: obj.testKLAgainstCategoricalDistribution(batch_size) without return types