code: def testGammaNonPositiveInitializationParamsRaises(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."
      ):
          gamma = gamma_lib.Gamma(
              concentration=concentration_v,
              rate=rate_v,
              validate_args=True,
          )
          self.evaluate(gamma.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."
      ):
          gamma = gamma_lib.Gamma(
              concentration=concentration_v,
              rate=rate_v,
              validate_args=True,
          )
          self.evaluate(gamma.mean())
  
imports: import functools
  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 gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import implementation_selection
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 27 for calling the GammaTest obj's underlying member method code with example usage: obj.testGammaNonPositiveInitializationParamsRaises() without return types