class_docstr: Tensor wrapper containing data necessary for sharding.
  
  The tensor representation used as inputs to pre-made and custom
  `tf.train.experiemental.ShardingCallback`s, which can be specified using the
  `experimental_sharding_callback` option in `tf.train.CheckpointOptions`.
code: def __hash__(self) -> int:
      return hash(
          (self.name, self.dtype, str(self.device), self.checkpoint_key)
      )
  
imports: import abc
  import dataclasses
  import inspect
  from typing import Hashable, MutableMapping, Sequence
  from tensorflow.python.framework import device
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.ops import variables
  from tensorflow.python.trackable import base
  from tensorflow.python.training.saving import saveable_object
  from tensorflow.python.util import tf_export
label_desc: Use image node_id 1 for calling the ShardableTensor obj's underlying member method code with example usage: obj.__hash__() and returns: hash