code: def _restore_slim(self, variables):
          
          variables_to_restore = self.get_variables_in_checkpoint_file(self.flags['RESTORE_SLIM_FILE'])
          variables_to_restore = {self.name_in_checkpoint(v): v for v in variables if (self.name_in_checkpoint(v) in variables_to_restore)}
          if variables_to_restore is []:
              print('Check the SLIM checkpoint filename. No model variables matched the checkpoint variables.')
              exit()
          saver = tf.train.Saver(variables_to_restore)
          saver.restore(self.sess, self.flags['RESTORE_SLIM_FILE'])
          print("Model restored from %s" % self.flags['RESTORE_SLIM_FILE'])
docs: Restore from tf-slim file (usually a ImageNet pre-trained model).