code: def test_log_prob_with_multiple_axes(self):
      if not JAX_MODE:
          self.skipTest("Multiple axes only supported in JAX backend.")
  
      other_axis_name = self.axis_name + "_other"
  
      @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, other_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, other_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), [2, 2]
              ).log_prob,
              (x,),
          )
          lp2, g2 = gradient.value_and_gradient(
              independent.Independent(
                  normal.Normal(tf.zeros([2, 2]), 1.0), 2
              ).log_prob,
              (x,),
          )
          return lp1, g1, lp2, g2
  
      x = tf.ones([2, 2])
  
      def outer_run(x):
          return self.strategy_run(
              lp_grad, (x,), axis_name=other_axis_name
          )
  
      lp1, g1, lp2, g2 = self.strategy_run(outer_run, (x,))
      true_lp1, true_g1, true_lp2, true_g2 = true_lp_grad(x)
  
      self.assertAllClose(tf.ones([2, 2]) * true_lp1, lp1)
      self.assertAllClose(true_g1[0], g1[0])
      self.assertAllClose(tf.ones([2, 2]) * true_lp2, lp2)
      self.assertAllClose(true_g2[0], g2[0])
  
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 7 for calling the ShardTest obj's underlying member method code with example usage: obj.test_log_prob_with_multiple_axes() and returns: lp1,  g1,  lp2,  g2, lp1,  g1,  lp2,  g2, self