Template Class class_

Inheritance Relationships

Base Type

  • public torch::detail::class_base

Class Documentation

template<class CurClass>
class class_ : public torch::detail::class_base

Entry point for custom C++ class registration.

To register a C++ class in PyTorch, instantiate torch::class_ with the desired class as the template parameter. Typically, this instantiation should be done in the initialization of a global variable, so that the class will be made available on dynamic library loading without any additional API calls needed. For example, to register a class named Foo, you might create a global variable like so:

static auto register_foo = torch::class_<Foo>("myclasses", "Foo")
  .def("myMethod", &Foo::myMethod)
  .def("lambdaMethod", [](const c10::intrusive_ptr<Foo>& self) {
    // Do something with `self`
In addition to registering the class, this registration also chains def() calls to register methods. myMethod() is registered with a pointer to the Foo class’s myMethod() method. lambdaMethod() is registered with a C++ lambda expression.

Public Functions

inline explicit class_(const std::string &namespaceName, const std::string &className, std::string doc_string = "")

This constructor actually registers the class type.

String argument namespaceName is an identifier for the namespace you would like this class to appear in. String argument className is the name you would like to see this class exposed as in Python and TorchScript. For example, if you pass foo as the namespace name and Bar as the className, the class will appear as in Python and TorchScript

template<typename ...Types>
inline class_ &def(torch::detail::types<void, Types...>, std::string doc_string = "", std::initializer_list<arg> default_args = {})

def() can be used in conjunction with torch::init() to register a constructor for a given C++ class type.

For example, passing torch::init<int, std::string>() would register a two-argument constructor taking an int and a std::string as argument.

template<typename Func, typename ...ParameterTypes>
inline class_ &def(InitLambda<Func, c10::guts::typelist::typelist<ParameterTypes...>> init, std::string doc_string = "", std::initializer_list<arg> default_args = {})
template<typename Func>
inline class_ &def(std::string name, Func f, std::string doc_string = "", std::initializer_list<arg> default_args = {})

This is the normal method registration API.

name is the name that the method will be made accessible by in Python and TorchScript. f is a callable object that defines the method. Typically f will either be a pointer to a method on CurClass, or a lambda expression that takes a c10::intrusive_ptr<CurClass> as the first argument (emulating a this argument in a C++ method.)


// Exposes method `foo` on C++ class `Foo` as `call_foo()` in
// Python and TorchScript
.def("call_foo", &Foo::foo)

// Exposes the given lambda expression as method `call_lambda()`
// in Python and TorchScript.
.def("call_lambda", [](const c10::intrusive_ptr<Foo>& self) {
  // do something

template<typename Func>
inline class_ &def_static(std::string name, Func func, std::string doc_string = "")

Method registration API for static methods.

template<typename GetterFunc, typename SetterFunc>
inline class_ &def_property(const std::string &name, GetterFunc getter_func, SetterFunc setter_func, std::string doc_string = "")

Property registration API for properties with both getter and setter functions.

template<typename GetterFunc>
inline class_ &def_property(const std::string &name, GetterFunc getter_func, std::string doc_string = "")

Property registration API for properties with only getter function.

template<typename T>
inline class_ &def_readwrite(const std::string &name, T CurClass::* field)

Property registration API for properties with read-write access.

template<typename T>
inline class_ &def_readonly(const std::string &name, T CurClass::* field)

Property registration API for properties with read-only access.

inline class_ &_def_unboxed(const std::string &name, std::function<void(jit::Stack&)> func, c10::FunctionSchema schema, std::string doc_string = "")

This is an unsafe method registration API added for adding custom JIT backend support via custom C++ classes.

It is not for general purpose use.

template<typename GetStateFn, typename SetStateFn>
inline class_ &def_pickle(GetStateFn &&get_state, SetStateFn &&set_state)

def_pickle() is used to define exactly what state gets serialized or deserialized for a given instance of a custom C++ class in Python or TorchScript.

This protocol is equivalent to the Pickle concept of __getstate__ and __setstate__ from Python (

Currently, both the get_state and set_state callables must be C++ lambda expressions. They should have the following signatures, where CurClass is the class you’re registering and T1 is some object that encapsulates the state of the object.

__getstate__(intrusive_ptr<CurClass>) -> T1
__setstate__(T2) -> intrusive_ptr<CurClass>
T1 must be an object that is convertable to IValue by the same rules for custom op/method registration.

For the common case, T1 == T2. T1 can also be a subtype of T2. An example where it makes sense for T1 and T2 to differ is if setstate handles legacy formats in a backwards compatible way.


    // __getstate__
    [](const c10::intrusive_ptr<MyStackClass<std::string>>& self) {
      return self->stack_;
    [](std::vector<std::string> state) { // __setstate__
       return c10::make_intrusive<MyStackClass<std::string>>(
          std::vector<std::string>{"i", "was", "deserialized"});


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources