code: def test_hmc_type_checks(self, model_fn):
      model = model_fn()
      pins = {"treatment_effects": tf.constant(TREATMENT_EFFECTS)}
  
      def do_sample(seed):
          return windowed_sampling.windowed_adaptive_hmc(
              3,
              model,
              num_leapfrog_steps=2,
              num_adaptation_steps=2,
              seed=seed,
              **pins,
          )
  
      draws, _ = do_sample(test_util.test_seed())
      self.evaluate(draws)
  
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 1 for calling the WindowedSamplingTest obj's underlying member method code with example usage: obj.test_hmc_type_checks(model_fn) and returns: windowed_sampling