I have a existing tensorflow saved model with the directory structure below. That is basically the only thing I have. **I can manage to get the checkpoint files though, but it would be great if we don’t need anything from training procedure**.

```
saved_model.pb
variables/
variables.data-00000-of-00001
variables.idnex
```

Now I would like to run the `tensorflow.python.tools.optimize_for_inference`

to optimize it for later inference in tf serving.

However, even though I can successfully optimize its graph_def, I cannot prepare appropriate variables for the new graph. The problem reside in the `Save`

and `Restore`

op, which exists in the old graph, but are pruned in the optimized graph_def.

But the variables file contains the save/restore op, it will give errors like

`Save/xxx`

does not exist in graph.

I tried to leverage the `graph_util.convert_variables_to_constant`

function, but I got the

Attempting to use unitialized node xxx/yyy

How can I prune the graph structure and make successfull predictions? Thanks.