code: def test_var(self):
      d = batch_broadcast.BatchBroadcast(
          normal.Normal(0.0, [[0.5], [1.0]]), [2, 3]
      )
      expected = tf.broadcast_to(tf.constant([[0.25], [1.0]]), [2, 3])
      self.assertAllEqual(expected, d.variance())
  
imports: import hypothesis
  import hypothesis.strategies
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import batch_broadcast
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.distributions import von_mises_fisher
  from tensorflow_probability.python.distributions import wishart
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.random import random_ops
label_desc: Use image node_id 7 for calling the _BatchBroadcastTest obj's underlying member method code with example usage: obj.test_var() without return types