code: def test_dtype_and_structure_jd_named(self):
      self.assertEqual(
          dict(x=tf.float32, y=tf.float32, z=tf.float32),
          jdp.JointDistributionPinned(jd_named(), w=1.0).dtype,
      )
      self.assertEqual(
          collections.OrderedDict(
              (("w", tf.float32), ("z", tf.float32))
          ),
          jdp.JointDistributionPinned(
              jd_named_ordered(), None, 1.0, [2.0, 3]
          ).dtype,
      )
      self.assertEqual(
          collections.OrderedDict(
              (("w", tf.float32), ("x", tf.float32), ("z", tf.float32))
          ),
          jdp.JointDistributionPinned(
              jd_named_ordered(), y=[2.0, 3]
          ).dtype,
      )
      self.assertEqual(
          structural_tuple.structtuple(["w", "y", "z"])(
              tf.float32, tf.float32, tf.float32
          ),
          jdp.JointDistributionPinned(
              jd_named_namedtuple(), x=2.0
          ).dtype,
      )
  
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 4 for calling the JointDistributionPinnedTest obj's underlying member method code with example usage: obj.test_dtype_and_structure_jd_named() without return types