code: def test_f64_state(self, method, method_kwargs):
      states, _ = callable_util.get_output_spec(
          lambda: method(  # pylint: disable=g-long-lambda
              5,
              normal.Normal(tf.constant(0.0, tf.float64), 1.0),
              n_chains=2,
              num_adaptation_steps=25,
              seed=test_util.test_seed(),
              **method_kwargs,
          )
      )
  
      self.assertEqual(tf.float64, states.dtype)
  
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 18 for calling the WindowedSamplingTest obj's underlying member method code with example usage: obj.test_f64_state(method, method_kwargs) without return types