code: def test_kl(self):
      a = mvn_diag.MultivariateNormalDiag(
          [tf.range(3.0)] * 4, tf.ones(3)
      )
      b = mvn_diag.MultivariateNormalDiag(
          [tf.range(3.0) + 0.5] * 4, tf.ones(3)
      )
      kl = kullback_leibler.kl_divergence(
          masked.Masked(a, tf.sequence_mask(3, 4)),
          masked.Masked(b, tf.sequence_mask(2, 4)),
      )
      kl2 = kullback_leibler.kl_divergence(
          masked.Masked(a, tf.sequence_mask(2, 4)),
          masked.Masked(b, tf.sequence_mask(3, 4)),
      )
      self.assertAllClose(a.kl_divergence(b)[:2], kl[:2])
      self.assertAllEqual(float("nan"), kl[2])
      self.assertAllEqual(0.0, kl[3])
      self.assertAllEqual(float("nan"), kl2[2])
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import masked
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import relaxed_onehot_categorical
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 8 for calling the MaskedTest obj's underlying member method code with example usage: obj.test_kl() without return types