code: def test_pin_broadcast_value_autobatched(self):
      def model():
          c0 = yield gamma.Gamma(1.0, rate=1.0, name="c0")  # []
          c1 = yield gamma.Gamma(1.0, rate=1, name="c1")  # []
          probs = yield sample.Sample(
              beta.Beta(c1, c0), 14, name="probs"
          )  # [14]
          yield binomial.Binomial(
              total_count=30, probs=probs, name="obs"
          )  # [14]
  
      pinned = jdab.JointDistributionCoroutineAutoBatched(
          model
      ).experimental_pin(obs=15.0 * tf.ones([14]))
      pinned_with_broadcast_value = (
          jdab.JointDistributionCoroutineAutoBatched(
              model
          ).experimental_pin(obs=15.0)
      )  # Scalar value for vector-valued RV.
      xs = self.evaluate(
          pinned.sample_unpinned([5], seed=test_util.test_seed())
      )
      self.assertAllClose(
          pinned.log_prob(xs), pinned_with_broadcast_value.log_prob(xs)
      )
  
imports: import collections
  import functools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import independent
  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 multinomial
  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.experimental.distributions import increment_log_prob
  from tensorflow_probability.python.experimental.distributions import joint_distribution_pinned
  from tensorflow_probability.python.internal import structural_tuple
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import generic
label_desc: Use image node_id 13 for calling the JointDistributionPinnedTest obj's underlying member method code with example usage: obj.test_pin_broadcast_value_autobatched() without return types