code: def testNormalConjugateKnownSigmaPosteriorND(self):
      batch_size = 6
      mu0 = tf.constant([[3.0, -3.0]] * batch_size)
      sigma0 = tf.constant(
          [[math.sqrt(10.0), math.sqrt(15.0)]] * batch_size
      )
      sigma = tf.constant([[math.sqrt(2.0)]] * batch_size)
      x = tf.transpose(
          a=tf.constant(
              [[-2.5, 2.5, 4.0, 0.0, -1.0, 2.0]], dtype=tf.float32
          )
      )
      s = tf.reduce_sum(x)
      n = tf.size(x)
      prior = normal.Normal(loc=mu0, scale=sigma0)
      posterior = ncp.normal_conjugates_known_scale_posterior(
          prior=prior, scale=sigma, s=s, n=n
      )
  
      # Smoke test
      self.assertIsInstance(posterior, normal.Normal)
      posterior_log_pdf = self.evaluate(posterior.log_prob(x))
      self.assertEqual(posterior_log_pdf.shape, (6, 2))
  
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 2 for calling the NormalTest obj's underlying member method code with example usage: obj.testNormalConjugateKnownSigmaPosteriorND() without return types