class_docstr: Well tested because this is a simple override of the base class.
code: def testSingularScaleRaises(self):
      mu = [-1.0, 1]
      diag = [1.0, 0]
      with self.assertRaisesOpError("Singular"):
          dist = mvn_diag.MultivariateNormalDiag(
              mu, diag, validate_args=True
          )
          self.evaluate(dist.sample(seed=test_util.test_seed()))
  
imports: import os
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 9 for calling the MultivariateNormalDiagTest obj's underlying member method code with example usage: obj.testSingularScaleRaises() without return types