code: def testAssertsPositiveScale(self):
      loc, scale = (
          tf.Variable([-1, 0, 1], dtype=tf.float64),
          tf.Variable([-1, 2, 3], dtype=tf.float64),
      )
      with self.assertRaisesOpError(
          "Argument `scale` must be positive."
      ):
          d = cauchy.Cauchy(loc, scale, validate_args=True)
          self.evaluate([v.initializer for v in d.variables])
          self.evaluate(d.sample(seed=test_util.test_seed()))
  
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 28 for calling the CauchyTest obj's underlying member method code with example usage: obj.testAssertsPositiveScale() without return types