code: def test_kahan_broadcasting_check(self):
      def model():
          _ = yield normal.Normal(0.0, 1.0)  # Batch shape ()
          _ = yield normal.Normal(
              [0.0, 1.0, 2.0], 1.0
          )  # Batch shape [3]
  
      dist = jdab.JointDistributionCoroutineAutoBatched(
          model,
          validate_args=True,
          experimental_use_kahan_sum=True,
          batch_ndims=1,
      )
      sample = self.evaluate(
          dist.sample(
              seed=test_util.test_seed(sampler_type="stateless")
          )
      )
      with self.assertRaises(ValueError):
          self.evaluate(dist.log_prob(sample))
  
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 23 for calling the JointDistributionAutoBatchedTest obj's underlying member method code with example usage: obj.test_kahan_broadcasting_check() without return types