code: def testAssertsPositiveScaleAfterMutation(self):
      scale = tf.Variable([1.0, 2.0, 3.0])
      d = laplace.Laplace(loc=0.0, scale=scale, validate_args=True)
      self.evaluate([v.initializer for v in d.variables])
      with self.assertRaisesOpError(
          "Argument `scale` must be positive."
      ):
          with tf.control_dependencies(
              [scale.assign([1.0, 2.0, -3.0])]
          ):
              self.evaluate(
                  laplace.Laplace(loc=0.0, scale=1.0).kl_divergence(d)
              )
  
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 23 for calling the LaplaceTest obj's underlying member method code with example usage: obj.testAssertsPositiveScaleAfterMutation() without return types