code: def check_latent_to_dist(self, architecture):
      with tf.Graph().as_default():
          x = tf.random_uniform(shape=(16, 5, 5, 32))
          hparams = hparam.HParams(architecture=architecture)
          x_prior = glow_ops.latent_to_dist(
              "split_prior", x, hparams=hparams, output_channels=64
          )
          mean_t, scale_t = x_prior.loc, x_prior.scale
          with tf.Session() as session:
              session.run(tf.global_variables_initializer())
              mean, scale = session.run([mean_t, scale_t])
              self.assertEqual(mean.shape, (16, 5, 5, 64))
              self.assertEqual(scale.shape, (16, 5, 5, 64))
              self.assertTrue(np.allclose(mean, 0.0))
              self.assertTrue(np.allclose(scale, 1.0))
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tempfile
  from absl.testing import parameterized
  import numpy
  from six.moves import range
  from six.moves import zip
  from tensor2tensor.models.research import glow
  from tensor2tensor.models.research import glow_ops
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import hparam
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 8 for calling the GlowOpsTest obj's underlying member method code with example usage: obj.check_latent_to_dist(architecture) without return types