class_docstr: A meta-bijector meant for use in an SPMD distributed context.
  
  `Sharded` is a meta-bijector that enables distributions to be used in SPMD
  programs. A `Sharded` bijector represents a bijector which acts on elements
  are split across a set of devices. The number of shards is the number of
  devices in the current TensorFlow DistributionStrategy or the provided JAX
  pmap axis.
code: def shard_axis_name(self):
      return self._shard_axis_name
  
file_path: probability/tensorflow_probability/python/experimental/bijectors/sharded.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.internal import distribute_lib
label_desc: Use image node_id 3 for calling the Sharded obj's underlying member method code with example usage: obj.shard_axis_name() and returns: self