code: def test_quantile(self):
      d = batch_broadcast.BatchBroadcast(
          normal.Normal(loc=[0.0, 1, 2], scale=0.5), [2, 1]
      )
      expected = tf.broadcast_to(tf.constant([0.0, 1, 2]), [2, 3])
      self.assertAllEqual(expected, d.quantile(0.5))
      x = d.quantile([[0.45], [0.55]])
      self.assertAllEqual(expected, tf.round(x))
      self.assertAllTrue(x[0] < x[1])
  
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 9 for calling the _BatchBroadcastTest obj's underlying member method code with example usage: obj.test_quantile() without return types