code: def test_subsidiary_bijector_pins(self):
      pinned0 = jdp.JointDistributionPinned(
          jd_coroutine(), x=1.0, z=tf.eye(4, batch_shape=[2])
      )
      pinned1 = jdp.JointDistributionPinned(jd_coroutine(), x=1.0)
      bij0 = pinned0.experimental_default_event_space_bijector()
      bij1 = pinned1.experimental_default_event_space_bijector(
          z=tf.eye(4, batch_shape=[2])
      )
      self.assertEqual(
          bij0.inverse_event_shape(pinned0.event_shape),
          bij1.inverse_event_shape(pinned0.event_shape),
      )
      self.assertEqual(
          bij0.inverse_event_shape(pinned1.event_shape[:-1]),
          bij1.inverse_event_shape(pinned1.event_shape[:-1]),
      )
      init = self.evaluate(
          tf.nest.map_structure(
              lambda shp: tf.random.uniform(
                  shp, -2.0, 2.0, seed=test_util.test_seed()
              ),
              bij0.inverse_event_shape(pinned0.event_shape),
          )
      )
      self.assertAllCloseNested(bij0.forward(init), bij1.forward(init))
  
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 11 for calling the JointDistributionPinnedTest obj's underlying member method code with example usage: obj.test_subsidiary_bijector_pins() without return types