class_docstr: Op with a custom checkpointing implementation.
  
  Defined as part of the test because the MutableHashTable Python code is
  currently in contrib.
class_docstr_tok: ["Op","with","a","custom","checkpointing","implementation",".","Defined","as","part","of","the","test","because","the","MutableHashTable","Python","code","is","currently","in","contrib","."]
code: def __init__(self, name, table_ref=None):
      if table_ref is None:
          self.table_ref = gen_lookup_ops.mutable_hash_table_v2(
              key_dtype=dtypes.string,
              value_dtype=dtypes.float32,
              name=name,
          )
      else:
          self.table_ref = table_ref
      self._name = name
      if not context.executing_eagerly():
          self._saveable = CheckpointedOp.CustomSaveable(self, name)
          ops_lib.add_to_collection(
              ops_lib.GraphKeys.SAVEABLE_OBJECTS, self._saveable
          )
  
file_path: tensorflow-upstream/tensorflow/python/training/saver_test_utils.py
imports: from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_lookup_ops
  from tensorflow.python.training import saver
label_desc: Use image node_id 1 to create a new CheckpointedOp object  with example: obj = CheckpointedOp(name, table_ref)