code: def test_explicit_init_samples(self):
      if tf.executing_eagerly() and not JAX_MODE:
          self.skipTest("Eager test is slow.")
      stream = test_util.test_seed_stream()
  
      # Compute everything in a function so it is consistent in graph mode
      @tf.function
      def do_sample():
          jd_model = jdn.JointDistributionNamed(
              {
                  "x": half_normal.HalfNormal(1.0),
                  "y": lambda x: normal.Normal(0.0, x),
              }
          )
          init = {"x": tf.ones(64)}
          return windowed_sampling.windowed_adaptive_hmc(
              10,
              jd_model,
              num_adaptation_steps=200,
              current_state=init,
              num_leapfrog_steps=5,
              discard_tuning=False,
              y=tf.constant(1.0),
              seed=stream(),
              trace_fn=None,
          )
  
      self.evaluate(do_sample())
  
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 7 for calling the WindowedSamplingTest obj's underlying member method code with example usage: obj.test_explicit_init_samples() and returns: windowed_sampling