code: def testStatsBroadcastWhenLocIsBiggerThanScale(self):
      batch_shape = (3,)
      event_shape = (4,)
      loc_shape = batch_shape + event_shape
      loc = self.rng.randn(*loc_shape)
  
      scale_shape = event_shape * 2
      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()), loc)
      cov = mvn.covariance()
      self.assertAllEqual(batch_shape + event_shape * 2, cov.shape)
      self.assertAllClose(
          self.evaluate(cov),
          self.evaluate(
              scale.matmul(scale, adjoint_arg=True).to_dense()
          )
          + np.zeros(loc_shape + (1,), dtype=np.float32),
      )
  
      entropy = mvn.entropy()
      self.assertAllEqual(batch_shape, entropy.shape)
  
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 9 for calling the MultivariateNormalLinearOperatorTest obj's underlying member method code with example usage: obj.testStatsBroadcastWhenLocIsBiggerThanScale() without return types