code: def testNormalConjugateKnownSigmaPredictive(self):
      batch_size = 6
      mu0 = tf.constant([3.0] * batch_size)
      sigma0 = tf.constant([math.sqrt(10.0)] * batch_size)
      sigma = tf.constant([math.sqrt(2.0)] * batch_size)
      x = tf.constant([-2.5, 2.5, 4.0, 0.0, -1.0, 2.0])
      s = tf.reduce_sum(x)
      n = tf.size(x)
      prior = normal.Normal(loc=mu0, scale=sigma0)
      predictive = ncp.normal_conjugates_known_scale_predictive(
          prior=prior, scale=sigma, s=s, n=n
      )
  
      # Smoke test
      self.assertIsInstance(predictive, normal.Normal)
      predictive_log_pdf = self.evaluate(predictive.log_prob(x))
      self.assertEqual(predictive_log_pdf.shape, (6,))
  
file_path: probability/tensorflow_probability/python/distributions/normal_conjugate_posteriors_test.py
imports: import math
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import fill_scale_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import normal_conjugate_posteriors
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 4 for calling the NormalTest obj's underlying member method code with example usage: obj.testNormalConjugateKnownSigmaPredictive() without return types