code: def testNonPositiveInitializationParamsRaises(self):
      concentration_v = tf.constant(0.0, name="concentration")
      rate_v = tf.constant(1.0, name="rate")
      with self.assertRaisesOpError(
          "Argument `concentration` must be positive."
      ):
          d = exp_gamma_lib.ExpGamma(
              concentration=concentration_v,
              rate=rate_v,
              validate_args=True,
          )
          self.evaluate(d.mean())
      concentration_v = tf.constant(1.0, name="concentration")
      rate_v = tf.constant(0.0, name="rate")
      with self.assertRaisesOpError(
          "Argument `rate` must be positive."
      ):
          d = exp_gamma_lib.ExpGamma(
              concentration=concentration_v,
              rate=rate_v,
              validate_args=True,
          )
          self.evaluate(d.mean())
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import misc
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.distributions import exp_gamma
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 21 for calling the ExpGammaTest obj's underlying member method code with example usage: obj.testNonPositiveInitializationParamsRaises() without return types