code: def testLaplaceLaplaceKL(self):
      batch_size = 6
      event_size = 3
  
      a_loc = np.array(
          [[0.5] * event_size] * batch_size, dtype=np.float32
      )
      a_scale = np.array(
          [[0.1] * event_size] * batch_size, dtype=np.float32
      )
      b_loc = np.array(
          [[0.4] * event_size] * batch_size, dtype=np.float32
      )
      b_scale = np.array(
          [[0.2] * event_size] * batch_size, dtype=np.float32
      )
  
      a = laplace.Laplace(loc=a_loc, scale=a_scale, validate_args=True)
      b = laplace.Laplace(loc=b_loc, scale=b_scale, validate_args=True)
  
      distance = tf.abs(a_loc - b_loc)
      ratio = a_scale / b_scale
      true_kl = (
          -tf.math.log(ratio)
          - 1
          + distance / b_scale
          + ratio * tf.exp(-distance / a_scale)
      )
  
      kl = kullback_leibler.kl_divergence(a, b)
  
      x = a.sample(int(1e4), seed=test_util.test_seed())
      kl_samples = a.log_prob(x) - b.log_prob(x)
  
      true_kl_, kl_, kl_samples_ = self.evaluate(
          [true_kl, kl, kl_samples]
      )
      self.assertAllClose(kl_, true_kl_, atol=1e-5, rtol=1e-5)
      self.assertAllMeansClose(
          kl_samples_, true_kl_, axis=0, atol=0.0, rtol=1e-1
      )
  
      zero_kl = kullback_leibler.kl_divergence(a, a)
      true_zero_kl_, zero_kl_ = self.evaluate(
          [tf.zeros_like(true_kl), zero_kl]
      )
      self.assertAllEqual(true_zero_kl_, zero_kl_)
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import laplace
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 21 for calling the LaplaceTest obj's underlying member method code with example usage: obj.testLaplaceLaplaceKL() without return types