code: def test_extra_pins_not_required(self):
      model = jds.JointDistributionSequential(
          [
              normal.Normal(0.0, 1.0, name="x"),
              lambda x: normal.Normal(x, 1.0, name="y"),
          ]
      )
      pinned = model.experimental_pin(y=4.2)
  
      # No explicit pins are passed, since the model is already pinned.
      _, init, _, _, _, _ = windowed_sampling._setup_mcmc(
          model=pinned, n_chains=[20], seed=test_util.test_seed()
      )
      self.assertLen(init, 1)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.distributions import autoregressive
  from tensorflow_probability.python.distributions import beta_binomial
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import half_normal
  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 poisson
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental import distribute
  from tensorflow_probability.python.experimental.mcmc import windowed_sampling
  from tensorflow_probability.python.internal import callable_util
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import unnest
  from tensorflow_probability.python.mcmc import diagnostic
label_desc: Use image node_id 9 for calling the WindowedSamplingTest obj's underlying member method code with example usage: obj.test_extra_pins_not_required() without return types