code: def testNormalVariance(self):
      # sigma will be broadcast to [7, 7, 7]
      mu = [1.0, 2.0, 3.0]
      sigma = [7.0]
  
      normal = normal_lib.Normal(loc=mu, scale=sigma)
  
      self.assertAllEqual((3,), normal.variance().get_shape())
      self.assertAllEqual(
          [49.0, 49, 49], self.evaluate(normal.variance())
      )
  
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 23 for calling the NormalTest obj's underlying member method code with example usage: obj.testNormalVariance() without return types