code: def test_cov(self):
      d = batch_broadcast.BatchBroadcast(
          mvn_diag.MultivariateNormalDiag(tf.zeros(2), [0.5, 1.0]),
          [5, 3],
      )
      expected = tf.broadcast_to(
          tf.constant([[0.25, 0], [0, 1]]), [5, 3, 2, 2]
      )
      self.assertAllEqual(expected, d.covariance())
  
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 8 for calling the _BatchBroadcastTest obj's underlying member method code with example usage: obj.test_cov() without return types