code: def testConcentrationVariable(self):
      c = tf.Variable([1.0, 2.0])
      self.evaluate(c.initializer)
      d = pareto.Pareto(concentration=c, scale=1.0, validate_args=True)
      self.assertIs(d.concentration, c)
      self.evaluate(d.mean())
      with self.assertRaisesOpError("`concentration` must be positive"):
          with tf.control_dependencies([c.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 25 for calling the ParetoTest obj's underlying member method code with example usage: obj.testConcentrationVariable() without return types