def register_saver_ops(self):
variables = self.get_savable_variables()<EOL>if variables is None or len(variables) == <NUM_LIT:0>:<EOL><INDENT>self._saver = None<EOL>return<EOL><DEDENT>base_scope = self._get_base_variable_scope()<EOL>variables_map = {strip_name_scope(v.name, base_scope): v for v in variables}<EOL>self._saver = tf.train.Saver(<EOL>var_list=variables_map,<EOL>reshape=False,<EOL>sharded=False,<EOL>max_to_keep=<NUM_LIT:5>,<EOL>keep_checkpoint_every_n_hours=<NUM_LIT>,<EOL>name=None,<EOL>restore_sequentially=False,<EOL>saver_def=None,<EOL>builder=None,<EOL>defer_build=False,<EOL>allow_empty=True,<EOL>write_version=tf.train.SaverDef.V2,<EOL>pad_step_number=False,<EOL>save_relative_paths=True<EOL>)<EOL>
Registers the saver operations to the graph in context.