code: def test_repr_with_custom_sample_dtype(self):
      sd = collections.namedtuple("Model", ["s", "w"])(None, None)
  
      def dist():
          s = yield sample_lib.Sample(
              inverse_gamma.InverseGamma(2, 2), 100
          )
          yield normal.Normal(0, s)
  
      m = jdab.JointDistributionCoroutineAutoBatched(
          dist, sample_dtype=sd
      )
      self.assertEqual(
          (
              "<tfp.distributions.JointDistributionCoroutineAutoBatched"
              " 'JointDistributionCoroutineAutoBatched'"
              " batch_shape=[]"
              " event_shape=Model(s=[100], w=[100])"
              " dtype=Model(s=float32, w=float32)>"
          ),
          repr(m),
      )
  
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 17 for calling the JointDistributionAutoBatchedTest obj's underlying member method code with example usage: obj.test_repr_with_custom_sample_dtype() without return types