code: def testSampleVariance(self):
      seed_stream = test_util.test_seed_stream()
      loc, scale_row, scale_col = self._random_loc_and_scale(
          batch_shape=[5, 2],
          matrix_shape=[2, 3],
          seed_stream=seed_stream,
      )
      matrix_normal = mnlo.MatrixNormalLinearOperator(
          loc, scale_row, scale_col
      )
      samples = matrix_normal.sample(int(2e5), seed=seed_stream())
      variance_, samples_ = self.evaluate(
          [matrix_normal.variance(), samples]
      )
      self.assertAllClose(
          variance_, np.var(samples_, axis=0), rtol=1e-2
      )
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.bijectors import transform_diagonal
  from tensorflow_probability.python.distributions import matrix_normal_linear_operator
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 9 for calling the _MatrixNormalTest obj's underlying member method code with example usage: obj.testSampleVariance() without return types