code: def testDistributionTapeSafety(self):
      log_precision = tf.Variable(0.0)
      mean_times_precision = tf.Variable(1.0)
  
      def normal_from_natural(log_precision, mean_times_precision):
          variance = 1.0 / tf.exp(log_precision)
          mean = mean_times_precision * variance
          return normal.Normal(loc=mean, scale=tf.sqrt(variance))
  
      dist = deferred_module.DeferredModule(
          normal_from_natural, log_precision, mean_times_precision
      )
      self.assertIn(log_precision, dist.trainable_variables)
      self.assertIn(mean_times_precision, dist.trainable_variables)
  
      with tf.GradientTape() as tape:
          lp = dist.log_prob(0.4)
      grads = tape.gradient(lp, dist.trainable_variables)
      self.assertLen(grads, 2)
      for grad in grads:
          self.assertIsNotNone(grad)
  
file_path: probability/tensorflow_probability/python/experimental/util/deferred_module_test.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.experimental.util import deferred_module
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 1 for calling the DeferredModuleTest obj's underlying member method code with example usage: obj.testDistributionTapeSafety() and returns: normal