Tips for improving performance of Python pretty-printer?

Hello,

I'm implementing a bunch of pretty-printers (in Python) to improve the
debuggability of C++ types in Apache Arrow C++:
https://github.com/apache/arrow/pull/12092

However, I'm seeing performance issues where inspecting even relatively
simple information is quite slow as soon as I use the "natural" way, by
calling public C++ APIs (e.g. object methods) using
`gdb.parse_and_eval()`.

So for now I'm resorting to inspect private implementation details,
even for types I don't control such as `std::string` or `std::vector`.
As an example, I have the following helper code (simplified below for
clarity):

```
class SharedPtr:

    def __init__(self, val):
        self.val = val
        try:
            # libstdc++ internals
            self._ptr = val['_M_ptr']
        except gdb.error:
            # fallback for other C++ standard libraries
            self._ptr = gdb.parse_and_eval(
                f"{for_evaluation(val)}.get()")

    def get(self):
        return self._ptr


def for_evaluation(val):
    """
    Return a parsable form of gdb.Value `val`
    """
    ty = gdb.types.get_basic_type(val.type)
    if ty.code == gdb.TYPE_CODE_PTR:
        # It's already a pointer, can represent it directly
        return f"(({ty}) ({val}))"
    if val.address is None:
        raise ValueError(f"Cannot further evaluate rvalue: {val}")
    return f"(* ({ty}*) ({val.address}))"
```

This works fine but:

1) I need to maintain a generic fallback for non-GNU libstdc++
implementations of the C++ standard library
2) The generic fallback (obviously) suffers from the original
performance problem

Is it expected that `gdb.parse_and_eval` has such a poor performance?
(I didn't run any timings, but as a rough estimate I estimate that it
takes around 100 ms for a relatively simple expression)

Regards

Antoine.