code: def testCauchyCauchyKLWithMC(self):
      batch_size = 6
      mu_a = np.array([3.0] * batch_size)
      gamma_a = np.array([1.0, 2.0, 3.0, 1.5, 2.5, 3.5])
      mu_b = np.array([-3.0] * batch_size)
      gamma_b = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0])
  
      c_a = cauchy.Cauchy(loc=mu_a, scale=gamma_a, validate_args=True)
      c_b = cauchy.Cauchy(loc=mu_b, scale=gamma_b, validate_args=True)
  
      kl = kullback_leibler.kl_divergence(c_a, c_b)
      reverse_kl = kullback_leibler.kl_divergence(c_b, c_a)
      kl_val = self.evaluate(kl)
      reverse_kl_val = self.evaluate(reverse_kl)
  
      x = c_a.sample(int(1e5), seed=test_util.test_seed())
      kl_samples = c_a.log_prob(x) - c_b.log_prob(x)
      kl_samples_ = self.evaluate(kl_samples)
  
      self.assertEqual(kl.shape, (batch_size,))
  
      # The Cauchy KL is symmetric
      self.assertAllMeansClose(
          kl_samples_, kl_val, axis=0, atol=0.0, rtol=1e-2
      )
      self.assertAllMeansClose(
          kl_samples_, reverse_kl_val, axis=0, atol=0.0, rtol=1e-2
      )
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import cauchy
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 31 for calling the CauchyTest obj's underlying member method code with example usage: obj.testCauchyCauchyKLWithMC() without return types