code: def testMeanAndCovariance(self):
      loc, scale = self._random_loc_and_scale(
          batch_shape=[3, 4], event_shape=[5]
      )
      mvn = mvn_linear_operator.MultivariateNormalLinearOperator(
          loc, scale, validate_args=True
      )
  
      self.assertAllEqual(self.evaluate(mvn.mean()), loc)
      self.assertAllClose(
          self.evaluate(mvn.covariance()),
          self.evaluate(
              scale.matmul(scale, adjoint_arg=True).to_dense()
          ),
      )
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.bijectors import transform_diagonal
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import mvn_linear_operator
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 8 for calling the MultivariateNormalLinearOperatorTest obj's underlying member method code with example usage: obj.testMeanAndCovariance() without return types