code: def test_experimental_is_sharded_coroutine(self):
      dist = distributions[0][1](self.axis_name)
      self.assertTupleEqual(
          dist.experimental_shard_axis_names,
          ([], [self.axis_name], [self.axis_name]),
      )
  
imports: from absl.testing import parameterized
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import scale
  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 lognormal
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.distribute import joint_distribution
  from tensorflow_probability.python.experimental.distribute import sharded
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 1 for calling the JointDistributionTest obj's underlying member method code with example usage: obj.test_experimental_is_sharded_coroutine() without return types