code: def _random_loc_and_scale(self, batch_shape, event_shape):
      # This ensures covariance is positive def.
      mat_shape = batch_shape + event_shape + event_shape
      scale = tf.linalg.LinearOperatorLowerTriangular(
          self._random_tril_matrix(mat_shape), is_non_singular=True
      )
      loc_shape = batch_shape + event_shape
      loc = self.rng.randn(*loc_shape)
      return loc, scale
  
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 3 for calling the MultivariateNormalLinearOperatorTest obj's underlying member method code with example usage: obj._random_loc_and_scale(batch_shape, event_shape) and returns: loc,  scale