code: def testModifiedVariableAssertion(self):
      loc = 0.3
      scale = 2.0
      tailweight = tf.Variable(1.9, dtype=self.dtype)
      skewness = tf.Variable(1.2, dtype=self.dtype)
      self.evaluate([tailweight.initializer, skewness.initializer])
      normal_inverse_gaussian = nig.NormalInverseGaussian(
          loc=loc,
          scale=scale,
          tailweight=tailweight,
          skewness=skewness,
          validate_args=True,
      )
      with self.assertRaisesOpError("`tailweight > |skewness|`"):
          with tf.control_dependencies([tailweight.assign(1.0)]):
              self.evaluate(normal_inverse_gaussian.mean())
  
      tailweight = tf.Variable(1.9, dtype=self.dtype)
      skewness = tf.Variable(1.2, dtype=self.dtype)
      self.evaluate([tailweight.initializer, skewness.initializer])
      normal_inverse_gaussian = nig.NormalInverseGaussian(
          loc=loc,
          scale=scale,
          tailweight=tailweight,
          skewness=skewness,
          validate_args=True,
      )
      with self.assertRaisesOpError("`tailweight > |skewness|`"):
          with tf.control_dependencies([skewness.assign(-2.0)]):
              self.evaluate(normal_inverse_gaussian.mean())
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal_inverse_gaussian
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 12 for calling the _NormalInverseGaussianTest obj's underlying member method code with example usage: obj.testModifiedVariableAssertion() without return types