code: def _random_mu_and_sigma(self, batch_shape, event_shape):
      # This ensures sigma is positive def.
      mat_shape = batch_shape + event_shape + event_shape
      mat = self._rng.randn(*mat_shape)
      perm = np.arange(mat.ndim)
      perm[-2:] = [perm[-1], perm[-2]]
      sigma = np.matmul(mat, np.transpose(mat, perm))
  
      mu_shape = batch_shape + event_shape
      mu = self._rng.randn(*mu_shape)
  
      return mu, sigma
  
imports: import os
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  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 lognormal
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 14 for calling the MultivariateNormalTriLTest obj's underlying member method code with example usage: obj._random_mu_and_sigma(batch_shape, event_shape) and returns: mu,  sigma