code: def test_constructor_docstr(self):
      jd = jds.JointDistributionSequential(
          [
              normal.Normal(0.0, 1.0, name="z"),
              normal.Normal(0.0, 1.0, name="y"),
              lambda y, z: normal.Normal(y + z, 1.0, name="x"),
          ],
          validate_args=True,
      )
  
      # The following `__init__` styles are all permissible and produce
      # `JointDistributionPinned` objects behaving identically.
      PartialXY = collections.namedtuple("PartialXY", "x,y")
      PartialX = collections.namedtuple("PartialX", "x")
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, x=2.0).pins,
          jdp.JointDistributionPinned(jd, x=2.0, z=None).pins,
      )
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, x=2.0).pins,
          jdp.JointDistributionPinned(jd, dict(x=2.0)).pins,
      )
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, x=2.0).pins,
          jdp.JointDistributionPinned(jd, dict(x=2.0, y=None)).pins,
      )
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, x=2.0).pins,
          jdp.JointDistributionPinned(
              jd, collections.OrderedDict(x=2.0)
          ).pins,
      )
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, x=2.0).pins,
          jdp.JointDistributionPinned(
              jd, collections.OrderedDict((("x", 2.0), ("y", None)))
          ).pins,
      )
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, x=2.0).pins,
          jdp.JointDistributionPinned(
              jd, PartialXY(x=2.0, y=None)
          ).pins,
      )
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, x=2.0).pins,
          jdp.JointDistributionPinned(jd, PartialX(x=2.0)).pins,
      )
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, x=2.0).pins,
          jdp.JointDistributionPinned(jd, None, None, 2.0).pins,
      )
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, x=2.0).pins,
          jdp.JointDistributionPinned(jd, [None, None, 2.0]).pins,
      )
  
      # The 'care is taken to resolve any potential ambiguity' section...
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, 2.0).pins,
          jdp.JointDistributionPinned(jd, [2.0]).pins,
      )
      self.assertAllEqualNested(
          jdp.JointDistributionPinned(jd, np.array([2.0])).pins,
          jdp.JointDistributionPinned(jd, z=[2.0]).pins,
      )
  
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 1 for calling the JointDistributionPinnedTest obj's underlying member method code with example usage: obj.test_constructor_docstr() without return types