code: def _random_chol(self, *shape):
      mat = self._rng.rand(*shape)
      chol = transform_diagonal.TransformDiagonal(softplus.Softplus())(
          mat
      )
      chol = tf.linalg.band_part(chol, -1, 0)
      sigma = tf.matmul(chol, chol, adjoint_b=True)
      return self.evaluate(chol), self.evaluate(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 2 for calling the MultivariateNormalTriLSlicingTest obj's underlying member method code with example usage: obj._random_chol() and returns: self,  self