code: def testAssertsPositiveConcentrationAfterMutation(self):
      concentration = tf.Variable([1.0, 2.0, 3.0])
      self.evaluate(concentration.initializer)
      d = exp_gamma_lib.ExpGamma(
          concentration=concentration, rate=[5.0], validate_args=True
      )
      self.evaluate(d.sample(seed=test_util.test_seed()))
      with self.assertRaisesOpError(
          "Argument `concentration` must be positive."
      ):
          with tf.control_dependencies(
              [concentration.assign([1.0, 2.0, -3.0])]
          ):
              self.evaluate(d.sample(seed=test_util.test_seed()))
  
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 25 for calling the ExpGammaTest obj's underlying member method code with example usage: obj.testAssertsPositiveConcentrationAfterMutation() without return types