code: def test_smart_for_loop_uses_tf_while(self):
      dtype = np.float32
      true_mean = dtype([0, 0])
      true_cov = dtype([[1, 0.5], [0.5, 1]])
      true_cov_chol = np.linalg.cholesky(true_cov)
      num_results = 100
      counter = collections.Counter()
  
      def target_log_prob(x, y):
          counter["target_calls"] += 1
          z = tf.stack([x, y], axis=-1) - true_mean
          z = tf.linalg.triangular_solve(
              true_cov_chol, z[..., tf.newaxis]
          )[..., 0]
          return -0.5 * tf.reduce_sum(z**2.0, axis=-1)
  
      _ = step_kernel(
          num_steps=num_results,
          current_state=[dtype(-2), dtype(2)],
          kernel=hmc.HamiltonianMonteCarlo(
              target_log_prob_fn=target_log_prob,
              step_size=[0.5, 0.5],
              num_leapfrog_steps=2,
          ),
          return_final_kernel_results=False,
          seed=test_util.test_seed(),
      )
  
      self.assertAllEqual(dict(target_calls=2), counter)
  
file_path: probability/tensorflow_probability/python/experimental/mcmc/step_test.py
imports: import collections
  import warnings
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.mcmc.internal import test_fixtures
  from tensorflow_probability.python.experimental.mcmc.step import step_kernel
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import sample
label_desc: Use image node_id 6 for calling the StepKernelTest obj's underlying member method code with example usage: obj.test_smart_for_loop_uses_tf_while() and returns: unknown