code: def test_pinned_distribution_seq_args(self, jd_factory, sample_shape):
      s = jd_coroutine().sample(
          sample_shape,
          seed=test_util.test_seed(sampler_type="stateless"),
      )
      x, z = s.x, s.z
      underlying = jd_factory()
  
      tuple_args = (
          (
              None,
              x,
          ),
          (None, x, None, None),
          (None, x, None, z),
      )
      if jd_factory is jd_named or jd_factory is jd_named_autobatched:
          # JDNamed does not support unnamed args unless model is ordered.
          for args in tuple_args:
              with self.assertRaisesRegexp(ValueError, r"unordered"):
                  jdp.JointDistributionPinned(underlying, args)
              with self.assertRaisesRegexp(ValueError, r"unordered"):
                  jdp.JointDistributionPinned(underlying, *args)
          tuple_args = ()
  
      for args in tuple_args:
          # Use as args[0].
          pinned = jdp.JointDistributionPinned(underlying, args)
          self._check_pinning(pinned, sample_shape)
  
          # Use as *args.
          pinned = jdp.JointDistributionPinned(underlying, *args)
          self._check_pinning(pinned, sample_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 1 for calling the JointDistributionPinnedParameterizedTest obj's underlying member method code with example usage: obj.test_pinned_distribution_seq_args(jd_factory, sample_shape) without return types