code: def testEntropyWithBatch(self):
      logits = np.log(np.array([[0.2, 0.8], [0.6, 0.4]])) - 50.0
      dist = categorical.Categorical(logits, validate_args=True)
      self.assertAllClose(
          [
              -(0.2 * np.log(0.2) + 0.8 * np.log(0.8)),
              -(0.6 * np.log(0.6) + 0.4 * np.log(0.4)),
          ],
          self.evaluate(dist.entropy()),
          atol=0,
          rtol=1e-5,
      )
  
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 17 for calling the CategoricalTest obj's underlying member method code with example usage: obj.testEntropyWithBatch() without return types