code: def test_log_prob_multiple_samples(self, jd_class):
      models = {}
  
      def coroutine_model():
          a = yield bernoulli.Bernoulli(probs=0.5, dtype=tf.float32)
          b = yield bernoulli.Bernoulli(
              probs=0.25 + 0.5 * a, dtype=tf.float32
          )
          yield normal.Normal(loc=a, scale=1.0 + b)
  
      models[
          jdab.JointDistributionCoroutineAutoBatched
      ] = coroutine_model
  
      models[jdab.JointDistributionSequentialAutoBatched] = [
          bernoulli.Bernoulli(probs=0.5, dtype=tf.float32),
          lambda a: bernoulli.Bernoulli(
              probs=0.25 + 0.5 * a, dtype=tf.float32
          ),
          lambda b, a: normal.Normal(loc=a, scale=1.0 + b),
      ]
  
      models[
          jdab.JointDistributionNamedAutoBatched
      ] = collections.OrderedDict(
          (
              ("a", bernoulli.Bernoulli(probs=0.5, dtype=tf.float32)),
              (
                  "b",
                  lambda a: bernoulli.Bernoulli(
                      probs=0.25 + 0.5 * a, dtype=tf.float32
                  ),
              ),
              ("c", lambda b, a: normal.Normal(loc=a, scale=1.0 + b)),
          )
      )
  
      joint = jd_class(models[jd_class], validate_args=True)
  
      z = joint.sample(4, seed=test_util.test_seed())
  
      log_prob = joint.log_prob(z)
  
      a, b, c = (
          z.values() if hasattr(z, "values") else z
      )  # pylint: disable=unbalanced-tuple-unpacking
  
      expected_log_prob = (
          np.log(0.5)
          + tf.math.log(
              b * (0.25 + 0.5 * a) + (1 - b) * (0.75 - 0.5 * a)
          )
          + -0.5 * ((c - a) / (1.0 + b)) ** 2
          - 0.5 * np.log(2.0 * np.pi)
          - tf.math.log((1.0 + b))
      )
  
      self.assertAllClose(*self.evaluate([log_prob, expected_log_prob]))
  
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 6 for calling the JointDistributionAutoBatchedTest obj's underlying member method code with example usage: obj.test_log_prob_multiple_samples(jd_class) without return types