code: def test_bijector(self):
      jd = jds.JointDistributionSequential(
          [
              uniform.Uniform(-1.0, 1.0),
              lambda a: uniform.Uniform(  # pylint:disable=g-long-lambda
                  a + tf.ones_like(a), a + tf.constant(2, a.dtype)
              ),
              lambda b, a: uniform.Uniform(a, b, name="c"),
          ]
      )
      bij = jd.experimental_default_event_space_bijector(a=-0.5, b=1.0)
      test_input = (0.5,)
      self.assertIs(type(jd.dtype), type(bij.inverse(test_input)))
      self.assertAllClose(
          2 / 3, tf.math.sigmoid(bij.inverse(test_input)[0])
      )
  
      @jdc.JointDistributionCoroutine
      def model():
          root = jdc.JointDistributionCoroutine.Root
          x = yield root(normal.Normal(0.0, 1.0, name="x"))
          y = yield root(gamma.Gamma(1.0, 1.0, name="y"))
          yield normal.Normal(x, y, name="z")
  
      bij = model.experimental_default_event_space_bijector(
          model.sample(seed=test_util.test_seed())[-1:]
      )
      self.assertAllCloseNested(
          structural_tuple.structtuple(["x", "y"])(1.0, 2.0),
          bij.forward((1.0, generic.softplus_inverse(2.0))),
      )
  
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 8 for calling the JointDistributionPinnedTest obj's underlying member method code with example usage: obj.test_bijector() without return types