code: def testModifiedVariableAssertion(self):
      c = tf.Variable(0.9)
      s = tf.Variable(2.0)
      self.evaluate([c.initializer, s.initializer])
      inv_gamma = inverse_gamma.InverseGamma(
          concentration=c, scale=s, validate_args=True
      )
      with self.assertRaisesOpError("`concentration` must be positive"):
          with tf.control_dependencies([c.assign(-2.0)]):
              self.evaluate(inv_gamma.mean())
  
      with self.assertRaisesOpError("`scale` must be positive"):
          with tf.control_dependencies([c.assign(0.9), s.assign(-2.0)]):
              self.evaluate(inv_gamma.mean())
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 23 for calling the InverseGammaTest obj's underlying member method code with example usage: obj.testModifiedVariableAssertion() without return types