class_docstr: Well tested because this is a simple override of the base class.
code: def testStddev(self, is_static):
      mvn = mvn_diag.MultivariateNormalDiag(
          loc=self.maybe_static(
              tf.zeros([2, 3], dtype=tf.float32), is_static
          ),
          validate_args=True,
      )
      self.assertAllClose(
          np.ones([2, 3], dtype=np.float32), self.evaluate(mvn.stddev())
      )
  
      mvn = mvn_diag.MultivariateNormalDiag(
          loc=self.maybe_static(
              tf.zeros([100, 3], dtype=tf.float32), is_static
          ),
          scale_diag=self.maybe_static([3.0, 3.0, 3.0], is_static),
          validate_args=True,
      )
      self.assertAllClose(
          np.array(3.0 * np.ones([100, 3])), self.evaluate(mvn.stddev())
      )
  
      mvn = mvn_diag.MultivariateNormalDiag(
          loc=tf.zeros([3], dtype=tf.float32),
          scale_diag=[[3.0, 2, 1], [4, 5, 6]],
          validate_args=True,
      )
      self.assertAllClose(
          np.array([[3.0, 2, 1], [4, 5, 6]]),
          self.evaluate(mvn.stddev()),
      )
  
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 13 for calling the MultivariateNormalDiagTest obj's underlying member method code with example usage: obj.testStddev(is_static) without return types