code: def testScaleVariable(self):
      s = tf.Variable([1.0, 2.0])
      self.evaluate(s.initializer)
      d = pareto.Pareto(concentration=1.0, scale=s, validate_args=True)
      self.assertIs(d.scale, s)
      self.evaluate(d.mean())
      with self.assertRaisesOpError("`scale` must be positive"):
          with tf.control_dependencies([s.assign([-1, 2.0])]):
              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 kullback_leibler
  from tensorflow_probability.python.distributions import pareto
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 26 for calling the ParetoTest obj's underlying member method code with example usage: obj.testScaleVariable() without return types