code: def test_model_with_nontrivial_batch_shape(
      self, jd_class, sampler_type
  ):
      models = {}
  
      def coroutine_model():
          g = yield lognormal.LogNormal(0.0, [1.0, 2.0])
          df = yield exponential.Exponential([1.0, 2.0])
          loc = yield sample_lib.Sample(normal.Normal(0, g), 20)
          yield student_t.StudentT(tf.expand_dims(df, -1), loc, 1)
  
      models[
          jdab.JointDistributionCoroutineAutoBatched
      ] = coroutine_model
  
      models[jdab.JointDistributionSequentialAutoBatched] = [
          lognormal.LogNormal(0.0, [1.0, 2.0]),
          exponential.Exponential([1.0, 2.0]),
          lambda _, g: sample_lib.Sample(normal.Normal(0, g), 20),
          lambda loc, df: student_t.StudentT(
              tf.expand_dims(df, -1), loc, 1
          ),
      ]
  
      models[
          jdab.JointDistributionNamedAutoBatched
      ] = collections.OrderedDict(
          (
              ("g", lognormal.LogNormal(0.0, [1.0, 2.0])),
              ("df", exponential.Exponential([1.0, 2.0])),
              (
                  "loc",
                  lambda g: sample_lib.Sample(normal.Normal(0, g), 20),
              ),
              (
                  "x",
                  lambda loc, df: student_t.StudentT(
                      tf.expand_dims(df, -1), loc, 1
                  ),
              ),
          )
      )
  
      joint = jd_class(
          models[jd_class], batch_ndims=1, validate_args=True
      )
  
      self.assertAllEqualNested(
          joint._model_flatten(joint.event_shape), [[], [], [20], [20]]
      )
      self.assertAllEqual(joint.batch_shape, [2])
  
      is_scalar = joint._model_flatten(joint.is_scalar_event())
      self.assertAllEqual(is_scalar[0], True)
      self.assertAllEqual(is_scalar[1], True)
      self.assertAllEqual(is_scalar[2], False)
      self.assertAllEqual(is_scalar[3], False)
  
      self.assertAllEqual(joint.is_scalar_batch(), False)
  
      batch_shape = self.evaluate(joint.batch_shape_tensor())
      self.assertAllEqual(batch_shape, [2])
  
      x = joint.sample(
          [5], seed=test_util.test_seed(sampler_type=sampler_type)
      )
      lp = self.evaluate(joint.log_prob(x))
      self.assertAllEqual(lp.shape, [5, 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 2 for calling the JointDistributionAutoBatchedTest obj's underlying member method code with example usage: obj.test_model_with_nontrivial_batch_shape(jd_class, sampler_type) without return types