code: def test_transform(self):
      mvn = mvn_diag.MultivariateNormalDiag(
          loc=[1.0, 2.0, 3.0], scale_diag=[1.0, 1.0, 1.0]
      )
      diag_variance = tf.constant([0.1, 1.0, 10.0])
  
      if self.use_default_momentum_distribution:
          momentum_distribution = None
      else:
          momentum_distribution = (
              mvnpflo.MultivariateNormalPrecisionFactorLinearOperator(
                  precision_factor=tf.linalg.LinearOperatorDiag(
                      tf.math.sqrt(diag_variance)
                  )
              )
          )
      hmc_kernel = phmc.PreconditionedHamiltonianMonteCarlo(
          target_log_prob_fn=mvn.log_prob,
          momentum_distribution=momentum_distribution,
          step_size=0.3,
          num_leapfrog_steps=10,
      )
  
      kernel = transformed_kernel.TransformedTransitionKernel(
          hmc_kernel, bijector=scale.Scale(tf.math.rsqrt(diag_variance))
      )
  
      draws = sample.sample_chain(
          110,
          tf.zeros(3),
          kernel=kernel,
          seed=test_util.test_seed(),
          trace_fn=None,
      )
      ess = diagnostic.effective_sample_size(
          draws[-100:],
          filter_threshold=0,
          filter_beyond_positive_pairs=False,
      )
  
      if not self.use_default_momentum_distribution:
          self.assertAllClose(ess, tf.fill([3], 100.0))
      else:
          self.assertLess(self.evaluate(tf.reduce_min(ess)), 100.0)
  
imports: import collections
  from absl.testing import parameterized
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_linear_operator
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import wishart
  from tensorflow_probability.python.experimental.distributions import mvn_precision_factor_linop
  from tensorflow_probability.python.experimental.mcmc import preconditioned_hmc
  from tensorflow_probability.python.internal import auto_composite_tensor
  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.internal import unnest
  from tensorflow_probability.python.mcmc import diagnostic
  from tensorflow_probability.python.mcmc import dual_averaging_step_size_adaptation
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import simple_step_size_adaptation
  from tensorflow_probability.python.mcmc import transformed_kernel
  from tensorflow_probability.python.mcmc.internal import leapfrog_integrator
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 6 for calling the _PreconditionedHMCTest obj's underlying member method code with example usage: obj.test_transform() without return types