code: def testNormalEntropy(self):
      mu_v = np.array([1.0, 1.0, 1.0])
      sigma_v = np.array([[1.0, 2.0, 3.0]]).T
      dist = normal.Normal(loc=mu_v, scale=sigma_v, validate_args=True)
  
      # scipy.sp_stats.norm cannot deal with these shapes.
      sigma_broadcast = mu_v * sigma_v
      expected_entropy = 0.5 * np.log(
          2 * np.pi * np.exp(1) * sigma_broadcast**2
      )
      entropy = dist.entropy()
      np.testing.assert_allclose(
          expected_entropy, self.evaluate(entropy)
      )
      self.assertAllEqual(
          self.evaluate(dist.batch_shape_tensor()), entropy.shape
      )
      self.assertAllEqual(
          self.evaluate(dist.batch_shape_tensor()),
          self.evaluate(entropy).shape,
      )
      self.assertAllEqual(dist.batch_shape, entropy.shape)
      self.assertAllEqual(
          dist.batch_shape, self.evaluate(entropy).shape
      )
  
imports: import math
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 15 for calling the NormalTest obj's underlying member method code with example usage: obj.testNormalEntropy() without return types