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
      normal = normal_lib.Normal(loc=mu_v, scale=sigma_v)
  
      # scipy.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 = normal.entropy()
      np.testing.assert_allclose(
          expected_entropy, self.evaluate(entropy)
      )
      self.assertAllEqual(
          self.evaluate(normal.batch_shape_tensor()),
          entropy.get_shape(),
      )
      self.assertAllEqual(
          self.evaluate(normal.batch_shape_tensor()),
          self.evaluate(entropy).shape,
      )
      self.assertAllEqual(normal.batch_shape, entropy.get_shape())
      self.assertAllEqual(
          normal.batch_shape, self.evaluate(entropy).shape
      )
  
imports: import importlib
  import math
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.distributions import kullback_leibler
  from tensorflow.python.ops.distributions import normal
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 17 for calling the NormalTest obj's underlying member method code with example usage: obj.testNormalEntropy() without return types