code: def test_explicit_init(self):
      sample_dist = jds.JointDistributionSequential(
          [
              half_normal.HalfNormal(1.0, name=f"dist_{idx}")
              for idx in range(4)
          ]
      )
  
      explicit_init = [tf.ones(20) for _ in range(3)]
      _, init, bijector, _, _, _ = windowed_sampling._setup_mcmc(
          model=sample_dist,
          n_chains=[20],
          init_position=explicit_init,
          seed=test_util.test_seed(),
          dist_3=1.0,
      )
  
      self.assertAllEqual(
          self.evaluate(init),
          tf.convert_to_tensor(bijector(explicit_init)),
      )
  
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 6 for calling the WindowedSamplingTest obj's underlying member method code with example usage: obj.test_explicit_init() without return types