code: def testEntropy(self):
      samples = [
          [0],
          [0, 1, 0, 2, 2],
          [[0, 0], [0, 1], [1, 2]],
          [[[0, 0, 0, 1], [0, 0, 1, 1]], [[1, 1, 1, 2], [0, 1, 2, 2]]],
      ]
      expected_entropys = [
          entropy([1]),
          entropy([0.4, 0.2, 0.4]),
          [entropy([1]), entropy([0.5, 0.5]), entropy([0.5, 0.5])],
          [
              [entropy([0.75, 0.25]), entropy([0.5, 0.5])],
              [entropy([0.75, 0.25]), entropy([0.25, 0.25, 0.5])],
          ],
      ]
  
      for sample, expected_entropy in zip(samples, expected_entropys):
          input_ = tf.convert_to_tensor(value=sample, dtype=np.float64)
          input_ph = tf1.placeholder_with_default(
              input_, shape=input_.shape if self.static_shape else None
          )
          dist = empirical.Empirical(
              samples=input_ph, validate_args=True
          )
          self.assertAllClose(
              self.evaluate(dist.entropy()), expected_entropy
          )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import empirical
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 7 for calling the EmpiricalScalarTest obj's underlying member method code with example usage: obj.testEntropy() without return types