code: def testAssertsPositiveScaleAfterMutation(self):
      loc, scale = (
          tf.Variable([-1.0, 0.0, 1.0]),
          tf.Variable([1.0, 2.0, 3.0]),
      )
      d = cauchy.Cauchy(loc, 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(d.mean())
  
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 29 for calling the CauchyTest obj's underlying member method code with example usage: obj.testAssertsPositiveScaleAfterMutation() without return types