code: def test_f64_step_size(self):
      dist = jds.JointDistributionSequential(
          [
              normal.Normal(
                  tf.constant(0.0, dtype=tf.float64),
                  tf.constant(1.0, dtype=tf.float64),
              )
          ]
      )
      (
          target_log_prob_fn,
          initial_transformed_position,
          _,
          _,
          _,
          _,
      ) = windowed_sampling._setup_mcmc(
          dist,
          n_chains=[5],
          init_position=None,
          seed=test_util.test_seed(),
      )
      init_step_size = windowed_sampling._get_step_size(
          initial_transformed_position, target_log_prob_fn
      )
      self.assertDTypeEqual(init_step_size, np.float64)
      self.assertAllFinite(init_step_size)
  
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 12 for calling the WindowedSamplingTest obj's underlying member method code with example usage: obj.test_f64_step_size() without return types