code: def testNumAdaptationSteps(self):
      def target_log_prob_fn(x):
          return -(x**2)
  
      kernel = hmc.HamiltonianMonteCarlo(
          target_log_prob_fn=target_log_prob_fn,
          step_size=0.1,
          num_leapfrog_steps=1,
      )
      kernel = gbtla.GradientBasedTrajectoryLengthAdaptation(
          kernel,
          num_adaptation_steps=1,
          adaptation_rate=1.0,
          validate_args=True,
      )
  
      state = tf.zeros([64], self.dtype)
      seed = test_util.test_seed(sampler_type="stateless")
      step_0_kernel_results = kernel.bootstrap_results(state)
      state, step_1_kernel_results = kernel.one_step(
          state, step_0_kernel_results, seed=seed
      )
      _, step_2_kernel_results = kernel.one_step(
          state, step_1_kernel_results, seed=seed
      )
  
      (
          step_0_kernel_results,
          step_1_kernel_results,
          step_2_kernel_results,
      ) = self.evaluate(
          [
              step_0_kernel_results,
              step_1_kernel_results,
              step_2_kernel_results,
          ]
      )
  
      # The intention of num_adaptation_steps is that we should adapt for 1 step
      # and then hold the hyperparameters constant.
      self.assertGreater(
          np.abs(
              step_0_kernel_results.max_trajectory_length
              - step_1_kernel_results.max_trajectory_length
          ),
          0.005,
      )
      self.assertAllClose(
          step_1_kernel_results.max_trajectory_length,
          step_2_kernel_results.max_trajectory_length,
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import identity
  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 normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.experimental.mcmc import gradient_based_trajectory_length_adaptation
  from tensorflow_probability.python.experimental.mcmc import preconditioned_hmc
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.mcmc import dual_averaging_step_size_adaptation
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import transformed_kernel
label_desc: Use image node_id 8 for calling the _GradientBasedTrajectoryLengthAdaptationTest obj's underlying member method code with example usage: obj.testNumAdaptationSteps() and returns: unknown