Python Classes

PyO3 exposes a group of attributes powered by Rust's proc macro system for defining Python classes as Rust structs.

The main attribute is #[pyclass], which is placed upon a Rust struct to generate a Python type for it. A struct will usually also have one #[pymethods]-annotated impl block for the struct, which is used to define Python methods and constants for the generated Python type. (If the multiple-pymethods feature is enabled each #[pyclass] is allowed to have multiple #[pymethods] blocks.) Finally, there may be multiple #[pyproto] trait implementations for the struct, which are used to define certain python magic methods such as __str__.

This chapter will discuss the functionality and configuration these attributes offer. Below is a list of links to the relevant section of this chapter for each:

Defining a new class

To define a custom Python class, a Rust struct needs to be annotated with the #[pyclass] attribute.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
    debug: bool,
}
}

Because Python objects are freely shared between threads by the Python interpreter, all structs annotated with #[pyclass] must implement Send (unless annotated with #[pyclass(unsendable)]).

The above example generates implementations for PyTypeInfo, PyTypeObject, and PyClass for MyClass. To see these generated implementations, refer to the implementation details at the end of this chapter.

Adding the class to a module

Custom Python classes can then be added to a module using add_class().


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
   num: i32,
   debug: bool,
}
#[pymodule]
fn mymodule(_py: Python, m: &PyModule) -> PyResult<()> {
    m.add_class::<MyClass>()?;
    Ok(())
}
}

PyCell and interior mutability

You sometimes need to convert your pyclass into a Python object and access it from Rust code (e.g., for testing it). PyCell is the primary interface for that.

PyCell<T: PyClass> is always allocated in the Python heap, so Rust doesn't have ownership of it. In other words, Rust code can only extract a &PyCell<T>, not a PyCell<T>.

Thus, to mutate data behind &PyCell safely, PyO3 employs the Interior Mutability Pattern like RefCell.

Users who are familiar with RefCell can use PyCell just like RefCell.

For users who are not very familiar with RefCell, here is a reminder of Rust's rules of borrowing:

  • At any given time, you can have either (but not both of) one mutable reference or any number of immutable references.
  • References must always be valid.

PyCell, like RefCell, ensures these borrowing rules by tracking references at runtime.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
use pyo3::types::PyDict;
#[pyclass]
struct MyClass {
    #[pyo3(get)]
    num: i32,
    debug: bool,
}
Python::with_gil(|py| {
    let obj = PyCell::new(py, MyClass { num: 3, debug: true }).unwrap();
    {
        let obj_ref = obj.borrow(); // Get PyRef
        assert_eq!(obj_ref.num, 3);
        // You cannot get PyRefMut unless all PyRefs are dropped
        assert!(obj.try_borrow_mut().is_err());
    }
    {
        let mut obj_mut = obj.borrow_mut(); // Get PyRefMut
        obj_mut.num = 5;
        // You cannot get any other refs until the PyRefMut is dropped
        assert!(obj.try_borrow().is_err());
        assert!(obj.try_borrow_mut().is_err());
    }

    // You can convert `&PyCell` to a Python object
    pyo3::py_run!(py, obj, "assert obj.num == 5");
});
}

&PyCell<T> is bounded by the same lifetime as a GILGuard. To make the object longer lived (for example, to store it in a struct on the Rust side), you can use Py<T>, which stores an object longer than the GIL lifetime, and therefore needs a Python<'_> token to access.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}
fn return_myclass() -> Py<MyClass> {
    Python::with_gil(|py| Py::new(py, MyClass { num: 1 }).unwrap())
}
let obj = return_myclass();
Python::with_gil(|py|{
    let cell = obj.as_ref(py); // Py<MyClass>::as_ref returns &PyCell<MyClass>
    let obj_ref = cell.borrow(); // Get PyRef<T>
    assert_eq!(obj_ref.num, 1);
});
}

Customizing the class

The #[pyclass] macro accepts the following parameters:

  • name="XXX" - Set the class name shown in Python code. By default, the struct name is used as the class name.
  • freelist=XXX - The freelist parameter adds support of free allocation list to custom class. The performance improvement applies to types that are often created and deleted in a row, so that they can benefit from a freelist. XXX is a number of items for the free list.
  • gc - Classes with the gc parameter participate in Python garbage collection. If a custom class contains references to other Python objects that can be collected, the PyGCProtocol trait has to be implemented.
  • weakref - Adds support for Python weak references.
  • extends=BaseType - Use a custom base class. The base BaseType must implement PyTypeInfo.
  • subclass - Allows Python classes to inherit from this class.
  • dict - Adds __dict__ support, so that the instances of this type have a dictionary containing arbitrary instance variables.
  • unsendable - Making it safe to expose !Send structs to Python, where all object can be accessed by multiple threads. A class marked with unsendable panics when accessed by another thread.
  • module="XXX" - Set the name of the module the class will be shown as defined in. If not given, the class will be a virtual member of the builtins module.

Constructor

By default it is not possible to create an instance of a custom class from Python code. To declare a constructor, you need to define a method and annotate it with the #[new] attribute. Only Python's __new__ method can be specified, __init__ is not available.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}

#[pymethods]
impl MyClass {
    #[new]
    fn new(num: i32) -> Self {
        MyClass { num }
    }
}
}

Alternatively, if your new method may fail you can return PyResult<Self>.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}

#[pymethods]
impl MyClass {
    #[new]
    fn new(num: i32) -> PyResult<Self> {
        Ok(MyClass { num })
    }
}
}

If no method marked with #[new] is declared, object instances can only be created from Rust, but not from Python.

For arguments, see the Method arguments section below.

Return type

Generally, #[new] method have to return T: Into<PyClassInitializer<Self>> or PyResult<T> where T: Into<PyClassInitializer<Self>>.

For constructors that may fail, you should wrap the return type in a PyResult as well. Consult the table below to determine which type your constructor should return:

Cannot failMay fail
No inheritanceTPyResult<T>
Inheritance(T Inherits U)(T, U)PyResult<(T, U)>
Inheritance(General Case)PyClassInitializer<T>PyResult<PyClassInitializer<T>>

Inheritance

By default, PyAny is used as the base class. To override this default, use the extends parameter for pyclass with the full path to the base class.

For convenience, (T, U) implements Into<PyClassInitializer<T>> where U is the baseclass of T. But for more deeply nested inheritance, you have to return PyClassInitializer<T> explicitly.

To get a parent class from a child, use PyRef instead of &self for methods, or PyRefMut instead of &mut self. Then you can access a parent class by self_.as_ref() as &Self::BaseClass, or by self_.into_super() as PyRef<Self::BaseClass>.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;

#[pyclass(subclass)]
struct BaseClass {
    val1: usize,
}

#[pymethods]
impl BaseClass {
    #[new]
    fn new() -> Self {
        BaseClass { val1: 10 }
    }

    pub fn method(&self) -> PyResult<usize> {
        Ok(self.val1)
    }
}

#[pyclass(extends=BaseClass, subclass)]
struct SubClass {
    val2: usize,
}

#[pymethods]
impl SubClass {
    #[new]
    fn new() -> (Self, BaseClass) {
        (SubClass { val2: 15 }, BaseClass::new())
    }

    fn method2(self_: PyRef<Self>) -> PyResult<usize> {
        let super_ = self_.as_ref();  // Get &BaseClass
        super_.method().map(|x| x * self_.val2)
    }
}

#[pyclass(extends=SubClass)]
struct SubSubClass {
    val3: usize,
}

#[pymethods]
impl SubSubClass {
    #[new]
    fn new() -> PyClassInitializer<Self> {
        PyClassInitializer::from(SubClass::new())
            .add_subclass(SubSubClass{val3: 20})
    }

    fn method3(self_: PyRef<Self>) -> PyResult<usize> {
        let v = self_.val3;
        let super_ = self_.into_super();  // Get PyRef<SubClass>
        SubClass::method2(super_).map(|x| x * v)
    }
}
Python::with_gil(|py| {
    let subsub = pyo3::PyCell::new(py, SubSubClass::new()).unwrap();
    pyo3::py_run!(py, subsub, "assert subsub.method3() == 3000")
});
}

You can also inherit native types such as PyDict, if they implement PySizedLayout. However, this is not supported when building for the Python limited API (aka the abi3 feature of PyO3).

However, because of some technical problems, we don't currently provide safe upcasting methods for types that inherit native types. Even in such cases, you can unsafely get a base class by raw pointer conversion.


#![allow(unused)]
fn main() {
#[cfg(not(Py_LIMITED_API))] {
use pyo3::prelude::*;
use pyo3::types::PyDict;
use pyo3::{AsPyPointer, PyNativeType};
use std::collections::HashMap;

#[pyclass(extends=PyDict)]
#[derive(Default)]
struct DictWithCounter {
    counter: HashMap<String, usize>,
}

#[pymethods]
impl DictWithCounter {
    #[new]
    fn new() -> Self {
        Self::default()
    }
    fn set(mut self_: PyRefMut<Self>, key: String, value: &PyAny) -> PyResult<()> {
        self_.counter.entry(key.clone()).or_insert(0);
        let py = self_.py();
        let dict: &PyDict = unsafe { py.from_borrowed_ptr_or_err(self_.as_ptr())? };
        dict.set_item(key, value)
    }
}
Python::with_gil(|py| {
    let cnt = pyo3::PyCell::new(py, DictWithCounter::new()).unwrap();
    pyo3::py_run!(py, cnt, "cnt.set('abc', 10); assert cnt['abc'] == 10")
});
}
}

If SubClass does not provide a baseclass initialization, the compilation fails.

# use pyo3::prelude::*;

#[pyclass]
struct BaseClass {
    val1: usize,
}

#[pyclass(extends=BaseClass)]
struct SubClass {
    val2: usize,
}

#[pymethods]
impl SubClass {
    #[new]
    fn new() -> Self {
        SubClass { val2: 15 }
    }
}

Object properties

PyO3 supports two ways to add properties to your #[pyclass]:

  • For simple fields with no side effects, a #[pyo3(get, set)] attribute can be added directly to the field definition in the #[pyclass].
  • For properties which require computation you can define #[getter] and #[setter] functions in the #[pymethods] block.

We'll cover each of these in the following sections.

Object properties using #[pyo3(get, set)]

For simple cases where a member variable is just read and written with no side effects, you can declare getters and setters in your #[pyclass] field definition using the pyo3 attribute, like in the example below:


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    #[pyo3(get, set)]
    num: i32
}
}

The above would make the num field available for reading and writing as a self.num Python property. To expose the property with a different name to the field, specify this alongside the rest of the options, e.g. #[pyo3(get, set, name = "custom_name")].

Properties can be readonly or writeonly by using just #[pyo3(get)] or #[pyo3(set)] respectively.

To use these annotations, your field type must implement some conversion traits:

  • For get the field type must implement both IntoPy<PyObject> and Clone.
  • For set the field type must implement FromPyObject.

Object properties using #[getter] and #[setter]

For cases which don't satisfy the #[pyo3(get, set)] trait requirements, or need side effects, descriptor methods can be defined in a #[pymethods] impl block.

This is done using the #[getter] and #[setter] attributes, like in the example below:


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}

#[pymethods]
impl MyClass {
    #[getter]
    fn num(&self) -> PyResult<i32> {
        Ok(self.num)
    }
}
}

A getter or setter's function name is used as the property name by default. There are several ways how to override the name.

If a function name starts with get_ or set_ for getter or setter respectively, the descriptor name becomes the function name with this prefix removed. This is also useful in case of Rust keywords like type (raw identifiers can be used since Rust 2018).


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}
#[pymethods]
impl MyClass {
    #[getter]
    fn get_num(&self) -> PyResult<i32> {
        Ok(self.num)
    }

    #[setter]
    fn set_num(&mut self, value: i32) -> PyResult<()> {
        self.num = value;
        Ok(())
    }
}
}

In this case, a property num is defined and available from Python code as self.num.

Both the #[getter] and #[setter] attributes accept one parameter. If this parameter is specified, it is used as the property name, i.e.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
   num: i32,
}
#[pymethods]
impl MyClass {
    #[getter(number)]
    fn num(&self) -> PyResult<i32> {
        Ok(self.num)
    }

    #[setter(number)]
    fn set_num(&mut self, value: i32) -> PyResult<()> {
        self.num = value;
        Ok(())
    }
}
}

In this case, the property number is defined and available from Python code as self.number.

Attributes defined by #[setter] or #[pyo3(set)] will always raise AttributeError on del operations. Support for defining custom del behavior is tracked in #1778.

Instance methods

To define a Python compatible method, an impl block for your struct has to be annotated with the #[pymethods] attribute. PyO3 generates Python compatible wrappers for all functions in this block with some variations, like descriptors, class method static methods, etc.

Since Rust allows any number of impl blocks, you can easily split methods between those accessible to Python (and Rust) and those accessible only to Rust. However to have multiple #[pymethods]-annotated impl blocks for the same struct you must enable the multiple-pymethods feature of PyO3.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}
#[pymethods]
impl MyClass {
    fn method1(&self) -> PyResult<i32> {
        Ok(10)
    }

    fn set_method(&mut self, value: i32) -> PyResult<()> {
        self.num = value;
        Ok(())
    }
}
}

Calls to these methods are protected by the GIL, so both &self and &mut self can be used. The return type must be PyResult<T> or T for some T that implements IntoPy<PyObject>; the latter is allowed if the method cannot raise Python exceptions.

A Python parameter can be specified as part of method signature, in this case the py argument gets injected by the method wrapper, e.g.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
    debug: bool,
}
#[pymethods]
impl MyClass {
    fn method2(&self, py: Python) -> PyResult<i32> {
        Ok(10)
    }
}
}

From the Python perspective, the method2 in this example does not accept any arguments.

Class methods

To create a class method for a custom class, the method needs to be annotated with the #[classmethod] attribute. This is the equivalent of the Python decorator @classmethod.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
use pyo3::types::PyType;
#[pyclass]
struct MyClass {
    num: i32,
    debug: bool,
}
#[pymethods]
impl MyClass {
    #[classmethod]
    fn cls_method(cls: &PyType) -> PyResult<i32> {
        Ok(10)
    }
}
}

Declares a class method callable from Python.

  • The first parameter is the type object of the class on which the method is called. This may be the type object of a derived class.
  • The first parameter implicitly has type &PyType.
  • For details on parameter-list, see the documentation of Method arguments section.
  • The return type must be PyResult<T> or T for some T that implements IntoPy<PyObject>.

Static methods

To create a static method for a custom class, the method needs to be annotated with the #[staticmethod] attribute. The return type must be T or PyResult<T> for some T that implements IntoPy<PyObject>.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
    debug: bool,
}
#[pymethods]
impl MyClass {
    #[staticmethod]
    fn static_method(param1: i32, param2: &str) -> PyResult<i32> {
        Ok(10)
    }
}
}

Class attributes

To create a class attribute (also called class variable), a method without any arguments can be annotated with the #[classattr] attribute. The return type must be T for some T that implements IntoPy<PyObject>.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {}
#[pymethods]
impl MyClass {
    #[classattr]
    fn my_attribute() -> String {
        "hello".to_string()
    }
}

Python::with_gil(|py| {
    let my_class = py.get_type::<MyClass>();
    pyo3::py_run!(py, my_class, "assert my_class.my_attribute == 'hello'")
});
}

Note that unlike class variables defined in Python code, class attributes defined in Rust cannot be mutated at all:

// Would raise a `TypeError: can't set attributes of built-in/extension type 'MyClass'`
pyo3::py_run!(py, my_class, "my_class.my_attribute = 'foo'")

If the class attribute is defined with const code only, one can also annotate associated constants:


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {}
#[pymethods]
impl MyClass {
    #[classattr]
    const MY_CONST_ATTRIBUTE: &'static str = "foobar";
}
}

Callable objects

To specify a custom __call__ method for a custom class, the method needs to be annotated with the #[call] attribute. Arguments of the method are specified as for instance methods.


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
use pyo3::types::PyTuple;
#[pyclass]
struct MyClass {
    num: i32,
    debug: bool,
}
#[pymethods]
impl MyClass {
    #[call]
    #[args(args="*")]
    fn __call__(&self, args: &PyTuple) -> PyResult<i32> {
        println!("MyClass has been called");
        Ok(self.num)
    }
}
}

Method arguments

By default, PyO3 uses function signatures to determine which arguments are required. Then it scans the incoming args and kwargs parameters. If it can not find all required parameters, it raises a TypeError exception. It is possible to override the default behavior with the #[args(...)] attribute. This attribute accepts a comma separated list of parameters in the form of attr_name="default value". Each parameter has to match the method parameter by name.

Each parameter can be one of the following types:

  • "*": var arguments separator, each parameter defined after "*" is a keyword-only parameter. Corresponds to python's def meth(*, arg1.., arg2=..).
  • args="*": "args" is var args, corresponds to Python's def meth(*args). Type of the args parameter has to be &PyTuple.
  • kwargs="**": "kwargs" receives keyword arguments, corresponds to Python's def meth(**kwargs). The type of the kwargs parameter has to be Option<&PyDict>.
  • arg="Value": arguments with default value. Corresponds to Python's def meth(arg=Value). If the arg argument is defined after var arguments, it is treated as a keyword-only argument. Note that Value has to be valid rust code, PyO3 just inserts it into the generated code unmodified.

Example:


#![allow(unused)]
fn main() {
use pyo3::prelude::*;
use pyo3::types::{PyDict, PyTuple};

#[pyclass]
struct MyClass {
    num: i32,
    debug: bool,
}
#[pymethods]
impl MyClass {
    #[new]
    #[args(num = "-1", debug = "true")]
    fn new(num: i32, debug: bool) -> Self {
        MyClass { num, debug }
    }

    #[args(
        num = "10",
        debug = "true",
        py_args = "*",
        name = "\"Hello\"",
        py_kwargs = "**"
    )]
    fn method(
        &mut self,
        num: i32,
        debug: bool,
        name: &str,
        py_args: &PyTuple,
        py_kwargs: Option<&PyDict>,
    ) -> PyResult<String> {
        self.debug = debug;
        self.num = num;
        Ok(format!(
            "py_args={:?}, py_kwargs={:?}, name={}, num={}, debug={}",
            py_args, py_kwargs, name, self.num, self.debug
        ))
    }

    fn make_change(&mut self, num: i32, debug: bool) -> PyResult<String> {
        self.num = num;
        self.debug = debug;
        Ok(format!("num={}, debug={}", self.num, self.debug))
    }
}
}

N.B. the position of the "*" argument (if included) controls the system of handling positional and keyword arguments. In Python:

import mymodule

mc = mymodule.MyClass()
print(mc.method(44, False, "World", 666, x=44, y=55))
print(mc.method(num=-1, name="World"))
print(mc.make_change(44, False))
print(mc.make_change(debug=False, num=-1))

Produces output:

py_args=('World', 666), py_kwargs=Some({'x': 44, 'y': 55}), name=Hello, num=44, debug=false
py_args=(), py_kwargs=None, name=World, num=-1, debug=true
num=44, debug=false
num=-1, debug=false

Implementation details

The #[pyclass] macros rely on a lot of conditional code generation: each #[pyclass] can optionally have a #[pymethods] block as well as several different possible #[pyproto] trait implementations.

To support this flexibility the #[pyclass] macro expands to a blob of boilerplate code which sets up the structure for "dtolnay specialization". This implementation pattern enables the Rust compiler to use #[pymethods] and #[pyproto] implementations when they are present, and fall back to default (empty) definitions when they are not.

This simple technique works for the case when there is zero or one implementations. To support multiple #[pymethods] for a #[pyclass] (in the multiple-pymethods feature), a registry mechanism provided by the inventory crate is used instead. This collects impls at library load time, but isn't supported on all platforms. See inventory: how it works for more details.

The #[pyclass] macro expands to roughly the code seen below. The PyClassImplCollector is the type used internally by PyO3 for dtolnay specialization:


#![allow(unused)]
fn main() {
#[cfg(not(feature = "multiple-pymethods"))] {
use pyo3::prelude::*;
// Note: the implementation differs slightly with the `multiple-pymethods` feature enabled.

/// Class for demonstration
struct MyClass {
    num: i32,
    debug: bool,
}

unsafe impl pyo3::PyTypeInfo for MyClass {
    type AsRefTarget = PyCell<Self>;

    const NAME: &'static str = "MyClass";
    const MODULE: Option<&'static str> = None;

    #[inline]
    fn type_object_raw(py: pyo3::Python) -> *mut pyo3::ffi::PyTypeObject {
        use pyo3::type_object::LazyStaticType;
        static TYPE_OBJECT: LazyStaticType = LazyStaticType::new();
        TYPE_OBJECT.get_or_init::<Self>(py)
    }
}

impl pyo3::pyclass::PyClass for MyClass {
    type Dict = pyo3::pyclass_slots::PyClassDummySlot;
    type WeakRef = pyo3::pyclass_slots::PyClassDummySlot;
    type BaseNativeType = PyAny;
}

impl pyo3::IntoPy<PyObject> for MyClass {
    fn into_py(self, py: pyo3::Python) -> pyo3::PyObject {
        pyo3::IntoPy::into_py(pyo3::Py::new(py, self).unwrap(), py)
    }
}

impl pyo3::class::impl_::PyClassImpl for MyClass {
    const DOC: &'static str = "Class for demonstration";
    const IS_GC: bool = false;
    const IS_BASETYPE: bool = false;
    const IS_SUBCLASS: bool = false;
    type Layout = PyCell<MyClass>;
    type BaseType = PyAny;
    type ThreadChecker = pyo3::class::impl_::ThreadCheckerStub<MyClass>;

    fn for_each_method_def(visitor: &mut dyn FnMut(&[pyo3::class::PyMethodDefType])) {
        use pyo3::class::impl_::*;
        let collector = PyClassImplCollector::<MyClass>::new();
        visitor(collector.py_methods());
        visitor(collector.py_class_descriptors());
        visitor(collector.object_protocol_methods());
        visitor(collector.async_protocol_methods());
        visitor(collector.context_protocol_methods());
        visitor(collector.descr_protocol_methods());
        visitor(collector.mapping_protocol_methods());
        visitor(collector.number_protocol_methods());
    }
    fn get_new() -> Option<pyo3::ffi::newfunc> {
        use pyo3::class::impl_::*;
        let collector = PyClassImplCollector::<Self>::new();
        collector.new_impl()
    }
    fn get_alloc() -> Option<pyo3::ffi::allocfunc> {
        use pyo3::class::impl_::*;
        let collector = PyClassImplCollector::<Self>::new();
        collector.alloc_impl()
    }
    fn get_free() -> Option<pyo3::ffi::freefunc> {
        use pyo3::class::impl_::*;
        let collector = PyClassImplCollector::<Self>::new();
        collector.free_impl()
    }
    fn get_call() -> Option<pyo3::ffi::PyCFunctionWithKeywords> {
        use pyo3::class::impl_::*;
        let collector = PyClassImplCollector::<Self>::new();
        collector.call_impl()
    }
    fn for_each_proto_slot(visitor: &mut dyn FnMut(&[pyo3::ffi::PyType_Slot])) {
        // Implementation which uses dtolnay specialization to load all slots.
        use pyo3::class::impl_::*;
        let collector = PyClassImplCollector::<Self>::new();
        visitor(collector.object_protocol_slots());
        visitor(collector.number_protocol_slots());
        visitor(collector.iter_protocol_slots());
        visitor(collector.gc_protocol_slots());
        visitor(collector.descr_protocol_slots());
        visitor(collector.mapping_protocol_slots());
        visitor(collector.sequence_protocol_slots());
        visitor(collector.async_protocol_slots());
        visitor(collector.buffer_protocol_slots());
    }

    fn get_buffer() -> Option<&'static pyo3::class::impl_::PyBufferProcs> {
        use pyo3::class::impl_::*;
        let collector = PyClassImplCollector::<Self>::new();
        collector.buffer_procs()
    }
}
Python::with_gil(|py| {
    let cls = py.get_type::<MyClass>();
    pyo3::py_run!(py, cls, "assert cls.__name__ == 'MyClass'")
});
}
}