code: def test_sample_with_value_as_kwarg(self):
      @jdab.JointDistributionCoroutineAutoBatched
      def dist():
          a = yield sample_lib.Sample(
              normal.Normal(0, 1.0), 2, name="a"
          )
          b = yield sample_lib.Sample(
              normal.Normal(0, 1.0), 3, name="b"
          )
          # The following line fails if not autovectorized.
          yield normal.Normal(
              a[tf.newaxis, ...] * b[..., tf.newaxis], 1.0, name="c"
          )
  
      x = self.evaluate(dist.sample(4, seed=test_util.test_seed()))
      x2 = self.evaluate(dist.sample(seed=test_util.test_seed(), a=x.a))
      self.assertAllClose(x.a, x2.a)
      self.assertAllEqual(x2.b.shape, [4, 3])
      self.assertAllEqual(x2.c.shape, [4, 3, 2])
  
imports: import collections
  import os
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import multinomial
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 11 for calling the JointDistributionAutoBatchedTest obj's underlying member method code with example usage: obj.test_sample_with_value_as_kwarg() without return types