code: def test_log_prob(self):
      @tf.function
      def lp_grad(x):
          lp1, g1 = gradient.value_and_gradient(
              sharded.Sharded(
                  sample_dist_lib.Sample(normal.Normal(0.0, 1.0), 1),
                  shard_axis_name=self.axis_name,
              ).log_prob,
              (x,),
          )
          lp2, g2 = gradient.value_and_gradient(
              sharded.Sharded(
                  independent.Independent(
                      normal.Normal(tf.zeros([1]), 1.0), 1
                  ),
                  shard_axis_name=self.axis_name,
              ).log_prob,
              (x,),
          )
          return lp1, g1, lp2, g2
  
      def true_lp_grad(x):
          lp1, g1 = gradient.value_and_gradient(
              sample_dist_lib.Sample(
                  normal.Normal(0.0, 1.0), test_lib.NUM_DEVICES
              ).log_prob,
              (x,),
          )
          lp2, g2 = gradient.value_and_gradient(
              independent.Independent(
                  normal.Normal(tf.zeros([test_lib.NUM_DEVICES]), 1.0),
                  1,
              ).log_prob,
              (x,),
          )
          return lp1, g1, lp2, g2
  
      x = tf.ones([test_lib.NUM_DEVICES])
      sharded_x = self.shard_values(x)
  
      lp1, g1, lp2, g2 = self.evaluate(
          self.per_replica_to_tensor(
              self.strategy_run(lp_grad, (sharded_x,))
          )
      )
      true_lp1, true_g1, true_lp2, true_g2 = self.evaluate(
          true_lp_grad(x)
      )
  
      self.assertAllClose(true_lp1, lp1[0])
      self.assertAllClose(true_g1, g1)
      self.assertAllClose(true_lp2, lp2[0])
      self.assertAllClose(true_g2, g2)
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import log_prob_ratio
  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.distribute import joint_distribution
  from tensorflow_probability.python.experimental.distribute import sharded
  from tensorflow_probability.python.experimental.distributions import increment_log_prob
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 6 for calling the ShardTest obj's underlying member method code with example usage: obj.test_log_prob() and returns: lp1,  g1,  lp2,  g2, lp1,  g1,  lp2,  g2