code: def testVariableScaleAssertions(self):
      loc = tf.constant([[1.0, 1.0]])
      scale_tensor = tf.Variable(tf.eye(2, dtype=np.float32))
      scale_row = tf.linalg.LinearOperatorLowerTriangular(
          tf.eye(1), is_non_singular=True
      )
      scale_column = tf.linalg.LinearOperatorLowerTriangular(
          scale_tensor, is_non_singular=True
      )
      d = mnlo.MatrixNormalLinearOperator(
          loc, scale_row, scale_column, validate_args=True
      )
      self.evaluate(scale_tensor.initializer)
      with self.assertRaises(Exception):
          with tf.control_dependencies(
              [scale_tensor.assign([[1.0, 0.0], [1.0, 0.0]])]
          ):
              self.evaluate(d.sample(seed=test_util.test_seed()))
  
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 11 for calling the _MatrixNormalTest obj's underlying member method code with example usage: obj.testVariableScaleAssertions() without return types