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 __repr__(self) -> str:
      return (
          f"\n{self.__class__.__name__}:\n"
          f"  _tensor_save_spec={self._tensor_save_spec!r}\n"
          f"  tensor={self.tensor!r}\n"
          f"  dtype={self.dtype!r}\n"
          f"  device={self.device!r}\n"
          f"  name={self.name!r}\n"
          f"  shape={self.shape!r}\n"
          f"  slice_spec={self.slice_spec!r}\n"
          f"  checkpoint_key={self.checkpoint_key!r}\n"
          f"  trackable={self.trackable!r}"
      )
  
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 2 for calling the ShardableTensor obj's underlying member method code with example usage: obj.__repr__() and returns: str, self, str, self, str, self, str, self, str, self, str, self, str, self, str, self, str, self, str, self