code: def testModifiedVariableAssertion(self):
      concentration = tf.Variable(0.9, dtype=self.dtype)
      loc = tf.Variable(1.2, dtype=self.dtype)
      self.evaluate([concentration.initializer, loc.initializer])
      dist = inverse_gaussian.InverseGaussian(
          loc=loc, concentration=concentration, validate_args=True
      )
      with self.assertRaisesOpError("`concentration` must be positive"):
          with tf.control_dependencies([concentration.assign(-2.0)]):
              self.evaluate(dist.mean())
      with self.assertRaisesOpError("`loc` must be positive"):
          with tf.control_dependencies(
              [loc.assign(-2.0), concentration.assign(2.0)]
          ):
              self.evaluate(dist.mean())
  
imports: import numpy
  from scipy import misc
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import inverse_gaussian
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 24 for calling the _InverseGaussianTest obj's underlying member method code with example usage: obj.testModifiedVariableAssertion() without return types