code: def _create_gaussian(self, gaussian_type):
      mu = tf.random_normal([3])
      if gaussian_type == tfp.distributions.MultivariateNormalDiag:
          scale_diag = tf.random_normal([3])
          dist = tfp.distributions.MultivariateNormalDiag(
              mu, scale_diag
          )
      if (
          gaussian_type
          == tfp.distributions.MultivariateNormalDiagPlusLowRank
      ):
          scale_diag = tf.random_normal([3])
          perturb_factor = tf.random_normal([3, 2])
          scale_perturb_diag = tf.random_normal([2])
          dist = tfp.distributions.MultivariateNormalDiagPlusLowRank(
              mu,
              scale_diag,
              scale_perturb_factor=perturb_factor,
              scale_perturb_diag=scale_perturb_diag,
          )
      if gaussian_type == tfp.distributions.MultivariateNormalTriL:
          cov = tf.random_uniform([3, 3], minval=0, maxval=1.0)
          # Create a PSD matrix.
          cov = 0.5 * (cov + tf.transpose(cov)) + 3 * tf.eye(3)
          scale = tf.cholesky(cov)
          dist = tfp.distributions.MultivariateNormalTriL(mu, scale)
      if (
          gaussian_type
          == tfp.distributions.MultivariateNormalFullCovariance
      ):
          cov = tf.random_uniform([3, 3], minval=0, maxval=1.0)
          # Create a PSD matrix.
          cov = 0.5 * (cov + tf.transpose(cov)) + 3 * tf.eye(3)
          dist = tfp.distributions.MultivariateNormalFullCovariance(
              mu, cov
          )
      return (dist, mu, dist.covariance())
  
file_path: trfl/trfl/distribution_ops_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  from absl.testing import parameterized
  import tensorflow.compat.v1
  import tensorflow_probability
  from trfl import distribution_ops
label_desc: Use image node_id 1 for calling the FactorisedKLGaussianTest obj's underlying member method code with example usage: obj._create_gaussian(gaussian_type) and returns: dist,  mu,  dist