code: def testNestedStepSizeError(self):
      kernel = hmc.HamiltonianMonteCarlo(
          target_log_prob_fn=lambda x: -(x**2),
          step_size=[0.1],
          num_leapfrog_steps=1,
      )
      kernel = gbtla.GradientBasedTrajectoryLengthAdaptation(
          kernel, num_adaptation_steps=100
      )
      with self.assertRaisesRegex(
          ValueError, "Step size must be a scalar"
      ):
          kernel.bootstrap_results([1.0])
  
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 2 for calling the GradientBasedTrajectoryLengthAdaptationTestGeneric obj's underlying member method code with example usage: obj.testNestedStepSizeError() without return types