class_docstr: Well tested because this is a simple override of the base class.
code: def testCovariance(self):
      mvn = mvn_diag.MultivariateNormalDiag(
          loc=tf.zeros([2, 3], dtype=tf.float32), validate_args=True
      )
      self.assertAllClose(
          self.evaluate(tf.eye(3, batch_shape=[2], dtype=tf.float32)),
          self.evaluate(mvn.covariance()),
      )
  
      mvn = mvn_diag.MultivariateNormalDiag(
          loc=tf.zeros([3], dtype=tf.float32),
          scale_diag=[[3.0, 2, 1], [4, 5, 6]],
          validate_args=True,
      )
      self.assertAllEqual([2], mvn.batch_shape)
      self.assertAllEqual([3], mvn.event_shape)
      self.assertAllClose(
          np.array(
              [
                  [[3.0, 0, 0], [0, 2, 0], [0, 0, 1]],
                  [[4, 0, 0], [0, 5, 0], [0, 0, 6]],
              ]
          )
          ** 2.0,
          self.evaluate(mvn.covariance()),
      )
  
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 11 for calling the MultivariateNormalDiagTest obj's underlying member method code with example usage: obj.testCovariance() without return types