class_docstr: DistributedValue backed by a DTensor instance.
  
  This class is useful to align the interface between DTensor and tf.distribute.
  Most of the tf.distribute API will accept/return DistributedValue, whereas
  DTensor low level API will only accept DTensor instance. In order to avoid
  the conversion back and forth between DistributedValue and DTensor, we
  introduce this class so that it can work with both side.
code: def values(self):
      # Note that this method exists so that it match the interface for PerReplica
      # The public API in `tf.types.experimental.distributed.PerReplica` doesn't
      # define any methods.
      return self._values
  
imports: from tensorflow.dtensor.python import accelerator_util
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import input_util
  from tensorflow.dtensor.python import layout
  from tensorflow.python.distribute import cross_device_ops
  from tensorflow.python.distribute import device_util
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import reduce_util
  from tensorflow.python.distribute import values
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion_registry
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import summary_ops_v2
label_desc: Use image node_id 3 for calling the DTensorDistributedValue obj's underlying member method code with example usage: obj.values() and returns: self