code: def testInvalidConcentration(self):
      scale = 1.0
      invalid_concentrations = [-0.01, 0.0, -2.0]
      for concentration in invalid_concentrations:
          with self.assertRaisesOpError(
              "`concentration` must be positive"
          ):
              dist = pareto.Pareto(
                  concentration, scale, validate_args=True
              )
              self.evaluate(dist.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 5 for calling the ParetoTest obj's underlying member method code with example usage: obj.testInvalidConcentration() without return types