code: def test_docstring_example(self):
      strm = test_util.test_seed_stream()
      dtype = np.float32
  
      nd = 50
      concentration = 1.0
  
      prior_dist = tfd.Sample(
          tfd.Normal(tf.constant(0.0, dtype), 100.0), nd
      )
  
      mu = tf.cast(np.linspace(-100, 100, nd), dtype=dtype)
      sigma = tf.cast(np.exp(np.linspace(-1, 1.5, nd)), dtype=dtype)
      corr_tril = tfd.CholeskyLKJ(
          dimension=nd, concentration=concentration
      ).sample(seed=strm())
      scale_tril = tf.linalg.matmul(tf.linalg.diag(sigma), corr_tril)
      target_dist = tfd.MultivariateStudentTLinearOperator(
          df=5.0,
          loc=mu,
          scale=tf.linalg.LinearOperatorLowerTriangular(scale_tril),
      )
  
      target_log_prob = lambda *x: (  # pylint: disable=g-long-lambda
          prior_dist.log_prob(*x) + target_dist.log_prob(*x)
      )
  
      (
          [mcmc_samples],
          diagnostic,
          conditioner,  # pylint: disable=unused-variable
      ) = turnkey_inference_candidate.window_tune_nuts_sampling(
          target_log_prob,
          [prior_dist.sample(2000, seed=strm())],
          seed=strm(),
          parallel_iterations=1,
      )
  
      self.assertAllClose(
          tf.reduce_mean(mcmc_samples, [0, 1]), mu, atol=0.3, rtol=0.05
      )
  
      self.assertAllClose(
          tf.math.reduce_std(mcmc_samples, [0, 1]),
          target_dist._stddev(),
          atol=0.5,
          rtol=0.1,
      )
  
imports: import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from discussion import turnkey_inference_candidate
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 1 for calling the WindowTuneNutsSamplingTest obj's underlying member method code with example usage: obj.test_docstring_example() without return types