code: def test_event_shape_and_structure_jd_named(self):
      self.assertEqual(
          dict(x=[], y=[2], z=[2, 4, 4]),
          jdp.JointDistributionPinned(jd_named(), w=1.0).event_shape,
      )
      self.assertEqual(
          collections.OrderedDict((("w", []), ("z", [2, 4, 4]))),
          jdp.JointDistributionPinned(
              jd_named_ordered(), None, 1.0, [2.0, 3]
          ).event_shape,
      )
      self.assertEqual(
          collections.OrderedDict(
              (("w", []), ("x", []), ("z", [2, 4, 4]))
          ),
          jdp.JointDistributionPinned(
              jd_named_ordered(), y=[2.0, 3]
          ).event_shape,
      )
      self.assertEqual(
          structural_tuple.structtuple(["w", "y", "z"])(
              [], [2], [2, 4, 4]
          ),
          jdp.JointDistributionPinned(
              jd_named_namedtuple(), x=2.0
          ).event_shape,
      )
  
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 5 for calling the JointDistributionPinnedTest obj's underlying member method code with example usage: obj.test_event_shape_and_structure_jd_named() without return types