code: def test_sharded_samples_differently_with_nested_axes(self):
      if not JAX_MODE:
          self.skipTest("Multiple axes only supported in JAX backend.")
  
      other_axis_name = self.axis_name + "_other"
  
      def run(key, _):
          return sharded.Sharded(
              sample_dist_lib.Sample(normal.Normal(0.0, 1.0), 1),
              shard_axis_name=[self.axis_name, other_axis_name],
          ).sample(seed=key)
  
      def outer_run(key, x):
          return self.strategy_run(
              run,
              (key, x),
              axis_name=other_axis_name,
              in_axes=(None, 0),
          )
  
      # Pass a dummy ones([2, 2]) to create the right axis sizes
      sample = self.strategy_run(
          outer_run, (self.key, tf.ones([2, 2])), in_axes=(None, 0)
      )
      for i in range(2):
          for j in range(2):
              for k in range(2):
                  for l in range(2):
                      if i == k and j == l:
                          continue
                      self.assertNotEqual(sample[i, j], sample[k, l])
  
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 2 for calling the ShardTest obj's underlying member method code with example usage: obj.test_sharded_samples_differently_with_nested_axes() and returns: sharded, self