code: def testCorrectStepSizeTransformedkernel(self):
      strm = test_util.test_seed_stream()
      scalings = 0.1
      bijector = sigmoid.Sigmoid()
      prior = beta.Beta(0.1, 0.1)
      likelihood = beta.Beta(5.0, 5.0)
      init_state = [
          tf.clip_by_value(
              prior.sample(10000, seed=strm()), 1e-5, 1.0 - 1e-5
          )
      ]
      make_transform_kernel_fn = gen_make_transform_hmc_kernel_fn(
          [bijector], num_leapfrog_steps=1
      )
  
      kernel = make_transform_kernel_fn(
          likelihood.log_prob, init_state, scalings=scalings
      )
      step_size, expected_step_size = self.evaluate(
          [
              tf.squeeze(kernel.inner_kernel.step_size[0]),
              scalings
              * tf.math.reduce_std(bijector.inverse(init_state[0])),
          ]
      )
      self.assertAllGreater(step_size, 0.0)
      self.assertAllEqual(step_size, expected_step_size)
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import mixture
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.experimental.mcmc.sample_sequential_monte_carlo import compute_hmc_step_size
  from tensorflow_probability.python.experimental.mcmc.sample_sequential_monte_carlo import gen_make_hmc_kernel_fn
  from tensorflow_probability.python.experimental.mcmc.sample_sequential_monte_carlo import gen_make_transform_hmc_kernel_fn
  from tensorflow_probability.python.experimental.mcmc.sample_sequential_monte_carlo import make_rwmh_kernel_fn
  from tensorflow_probability.python.experimental.mcmc.sample_sequential_monte_carlo import sample_sequential_monte_carlo
  from tensorflow_probability.python.experimental.mcmc.sample_sequential_monte_carlo import simple_heuristic_tuning
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import nuts
label_desc: Use image node_id 1 for calling the SampleSequentialMonteCarloTest obj's underlying member method code with example usage: obj.testCorrectStepSizeTransformedkernel() without return types