code: def testMeanAndCovarianceAndVarAndStddevLocIsNone(self):
      loc = None
  
      scale_shape = (2, 4, 4)
      scale = tf.linalg.LinearOperatorLowerTriangular(
          self._random_tril_matrix(scale_shape), is_non_singular=True
      )
  
      mvn = mvn_linear_operator.MultivariateNormalLinearOperator(
          loc, scale, validate_args=True
      )
  
      self.assertAllEqual(
          self.evaluate(mvn.mean()), np.zeros((2, 4), dtype=np.float32)
      )
  
      expected_cov = self.evaluate(
          scale.matmul(scale, adjoint_arg=True).to_dense()
      )
      self.assertAllClose(self.evaluate(mvn.covariance()), expected_cov)
  
      expected_var = self.evaluate(tf.linalg.diag_part(expected_cov))
      self.assertAllClose(self.evaluate(mvn.variance()), expected_var)
      self.assertAllClose(
          self.evaluate(mvn.stddev()), expected_var**0.5
      )
  
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 10 for calling the MultivariateNormalLinearOperatorTest obj's underlying member method code with example usage: obj.testMeanAndCovarianceAndVarAndStddevLocIsNone() without return types