code: def testInverseGammaNonPositiveInitializationParamsRaises(self):
      alpha_v = tf.constant(0.0, name="alpha")
      beta_v = tf.constant(1.0, name="beta")
      with self.assertRaisesOpError("`concentration` must be positive"):
          inv_gamma = inverse_gamma.InverseGamma(
              concentration=alpha_v, scale=beta_v, validate_args=True
          )
          self.evaluate(inv_gamma.mean())
      alpha_v = tf.constant(1.0, name="alpha")
      beta_v = tf.constant(0.0, name="beta")
      with self.assertRaisesOpError("`scale` must be positive"):
          inv_gamma = inverse_gamma.InverseGamma(
              concentration=alpha_v, scale=beta_v, validate_args=True
          )
          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 22 for calling the InverseGammaTest obj's underlying member method code with example usage: obj.testInverseGammaNonPositiveInitializationParamsRaises() without return types