code: def testEntropyWithLargeNegLogits(self):
      num_categories = 11
      logits = np.array(
          [
              [-1e7] * num_categories,
              [-1e8] * num_categories,
              [-1e9] * num_categories,
          ],
          dtype=np.float32,
      )
      dist = categorical.Categorical(logits=logits, validate_args=True)
      dist_entropy = dist.entropy()
  
      ans = [np.log(num_categories)] * 3
      self.assertAllClose(self.evaluate(dist_entropy), ans)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 22 for calling the CategoricalTest obj's underlying member method code with example usage: obj.testEntropyWithLargeNegLogits() without return types