# 1. Estendendo Python com C ou C++

É muito fácil adicionar novos módulos embutidos ao Python, se você souber programar em C. Você pode adicionar módulos de extensão para fazer duas coisas que não podem ser feitas diretamente no Python: eles podem implementar novos nos tipos de objetos embutidos e eles podem chamar funções da biblioteca C e chamadas do sistema.

Para dar suporte a extensões, a API do Python API (Application Programmers Interface) define um conjunto de funções, macros e variáveis que fornecem acesso à maior parte dos aspectos do sistema de tempo de execução do Python. A API do Python pode ser incorporada em um arquivo fonte em C com a inclusão do cabeçalho `"Python.h"`.

A compilação de um módulo de extensão depende do uso pretendido e da configuração do sistema; detalhes serão dados nos próximos capítulos.

Nota

A interface de extensões em C é específica para o CPython, e módulos de extensão não funcionam em outras implementações do Python. Em muitos casos, é possível evitar a criação destas extensões em C e preservar a portabilidade para outras implementações. Por exemplo, se o seu caso de uso for o de fazer chamadas a funções em bibliotecas C ou chamadas de sistema, considere utilizar o módulo `ctypes` ou a biblioteca cffi ao invés de escrever código C personalizado. Esses módulos permitem escrever código Python que pode interoperar com código C e que é mais portável entre implementações do Python do que escrever e compilar um módulo de extensão em C.

## 1.1. Um Exemplo Simples

Vamos criar um módulo de extensão chamado `spam` (a comida favorita dos fãs de Monty Python…) e digamos que nosso objetivo seja criar uma interface em Python para a função da biblioteca C `system()` [1]. Essa função toma uma string de caracteres terminada em nulo como argumento e retorna um número inteiro. Queremos que essa função seja chamável a partir do Python como abaixo:

    >>> import spam
    >>> status = spam.system("ls -l")

Comece criando um arquivo chamado `spammodule.c`. (Historicamente, se um módulo for chamado `spam`, o arquivo C contendo sua implementação é chamado `spammodule.c`; se o nome do módulo for muito longo, como `spammify`, o nome do arquivo pode ser só `spammify.c`.)

As duas primeiras linhas do nosso arquivo podem ser:

    #define PY_SSIZE_T_CLEAN
    #include <Python.h>

o que carrega a API do Python (você pode adicionar um comentário descrevendo o propósito do módulo e uma nota de copyright, se desejar).

Nota

Uma vez que Python pode definir algumas definições de pré-processador que afetam os cabeçalhos padrão em alguns sistemas, você deve incluir `Python.h` antes de quaisquer cabeçalhos padrão serem incluídos.

`#define PY_SSIZE_T_CLEAN` was used to indicate that `Py_ssize_t` should be used in some APIs instead of `int`. It is not necessary since Python 3.13, but we keep it here for backward compatibility. See Strings e buffers for a description of this macro.

All user-visible symbols defined by `Python.h` have a prefix of `Py` or `PY`, except those defined in standard header files.

Dica

For backward compatibility, `Python.h` includes several standard header files. C extensions should include the standard headers that they use, and should not rely on these implicit includes. If using the limited C API version 3.13 or newer, the implicit includes are:

- `<assert.h>`

- `<intrin.h>` (on Windows)

- `<inttypes.h>`

- `<limits.h>`

- `<math.h>`

- `<stdarg.h>`

- `<wchar.h>`

- `<sys/types.h>` (if present)

If `Py_LIMITED_API` is not defined, or is set to version 3.12 or older, the headers below are also included:

- `<ctype.h>`

- `<unistd.h>` (on POSIX)

If `Py_LIMITED_API` is not defined, or is set to version 3.10 or older, the headers below are also included:

- `<errno.h>`

- `<stdio.h>`

- `<stdlib.h>`

- `<string.h>`

A próxima coisa que adicionamos ao nosso arquivo de módulo é a função C que será chamada quando a expressão Python `spam.system(string)` for avaliada (veremos em breve como ela acaba sendo chamada):

    static PyObject *
    spam_system(PyObject *self, PyObject *args)
    {
        const char *command;
        int sts;

        if (!PyArg_ParseTuple(args, "s", &command))
            return NULL;
        sts = system(command);
        return PyLong_FromLong(sts);
    }

There is a straightforward translation from the argument list in Python (for example, the single expression `"ls -l"`) to the arguments passed to the C function. The C function always has two arguments, conventionally named self and args.

O argumento self aponta para o objeto do módulo para funções de nível de módulo; para um método, ele apontaria para a instância do objeto.

The args argument will be a pointer to a Python tuple object containing the arguments. Each item of the tuple corresponds to an argument in the call’s argument list. The arguments are Python objects — in order to do anything with them in our C function we have to convert them to C values. The function `PyArg_ParseTuple()` in the Python API checks the argument types and converts them to C values. It uses a template string to determine the required types of the arguments as well as the types of the C variables into which to store the converted values. More about this later.

`PyArg_ParseTuple()` returns true (nonzero) if all arguments have the right type and its components have been stored in the variables whose addresses are passed. It returns false (zero) if an invalid argument list was passed. In the latter case it also raises an appropriate exception so the calling function can return `NULL` immediately (as we saw in the example).

## 1.2. Intermezzo: erros e exceções

An important convention throughout the Python interpreter is the following: when a function fails, it should set an exception condition and return an error value (usually `-1` or a `NULL` pointer). Exception information is stored in three members of the interpreter’s thread state. These are `NULL` if there is no exception. Otherwise they are the C equivalents of the members of the Python tuple returned by `sys.exc_info()`. These are the exception type, exception instance, and a traceback object. It is important to know about them to understand how errors are passed around.

A API Python define uma série de funções para definir vários tipos de exceções.

The most common one is `PyErr_SetString()`. Its arguments are an exception object and a C string. The exception object is usually a predefined object like `PyExc_ZeroDivisionError`. The C string indicates the cause of the error and is converted to a Python string object and stored as the “associated value” of the exception.

Another useful function is `PyErr_SetFromErrno()`, which only takes an exception argument and constructs the associated value by inspection of the global variable `errno`. The most general function is `PyErr_SetObject()`, which takes two object arguments, the exception and its associated value. You don’t need to `Py_INCREF()` the objects passed to any of these functions.

You can test non-destructively whether an exception has been set with `PyErr_Occurred()`. This returns the current exception object, or `NULL` if no exception has occurred. You normally don’t need to call `PyErr_Occurred()` to see whether an error occurred in a function call, since you should be able to tell from the return value.

When a function f that calls another function g detects that the latter fails, f should itself return an error value (usually `NULL` or `-1`). It should not call one of the `PyErr_*` functions — one has already been called by g. f’s caller is then supposed to also return an error indication to its caller, again without calling `PyErr_*`, and so on — the most detailed cause of the error was already reported by the function that first detected it. Once the error reaches the Python interpreter’s main loop, this aborts the currently executing Python code and tries to find an exception handler specified by the Python programmer.

(There are situations where a module can actually give a more detailed error message by calling another `PyErr_*` function, and in such cases it is fine to do so. As a general rule, however, this is not necessary, and can cause information about the cause of the error to be lost: most operations can fail for a variety of reasons.)

To ignore an exception set by a function call that failed, the exception condition must be cleared explicitly by calling `PyErr_Clear()`. The only time C code should call `PyErr_Clear()` is if it doesn’t want to pass the error on to the interpreter but wants to handle it completely by itself (possibly by trying something else, or pretending nothing went wrong).

Every failing `malloc()` call must be turned into an exception — the direct caller of `malloc()` (or `realloc()`) must call `PyErr_NoMemory()` and return a failure indicator itself. All the object-creating functions (for example, `PyLong_FromLong()`) already do this, so this note is only relevant to those who call `malloc()` directly.

Also note that, with the important exception of `PyArg_ParseTuple()` and friends, functions that return an integer status usually return a positive value or zero for success and `-1` for failure, like Unix system calls.

Finally, be careful to clean up garbage (by making `Py_XDECREF()` or `Py_DECREF()` calls for objects you have already created) when you return an error indicator!

The choice of which exception to raise is entirely yours. There are predeclared C objects corresponding to all built-in Python exceptions, such as `PyExc_ZeroDivisionError`, which you can use directly. Of course, you should choose exceptions wisely — don’t use `PyExc_TypeError` to mean that a file couldn’t be opened (that should probably be `PyExc_OSError`). If something’s wrong with the argument list, the `PyArg_ParseTuple()` function usually raises `PyExc_TypeError`. If you have an argument whose value must be in a particular range or must satisfy other conditions, `PyExc_ValueError` is appropriate.

You can also define a new exception that is unique to your module. The simplest way to do this is to declare a static global object variable at the beginning of the file:

    static PyObject *SpamError = NULL;

and initialize it by calling `PyErr_NewException()` in the module’s `Py_mod_exec` function (`spam_module_exec()`):

    SpamError = PyErr_NewException("spam.error", NULL, NULL);

Since `SpamError` is a global variable, it will be overwritten every time the module is reinitialized, when the `Py_mod_exec` function is called.

For now, let’s avoid the issue: we will block repeated initialization by raising an `ImportError`:

    static PyObject *SpamError = NULL;

    static int
    spam_module_exec(PyObject *m)
    {
        if (SpamError != NULL) {
            PyErr_SetString(PyExc_ImportError,
                            "cannot initialize spam module more than once");
            return -1;
        }
        SpamError = PyErr_NewException("spam.error", NULL, NULL);
        if (PyModule_AddObjectRef(m, "SpamError", SpamError) < 0) {
            return -1;
        }

        return 0;
    }

    static PyModuleDef_Slot spam_module_slots[] = {
        {Py_mod_exec, spam_module_exec},
        {0, NULL}
    };

    static struct PyModuleDef spam_module = {
        .m_base = PyModuleDef_HEAD_INIT,
        .m_name = "spam",
        .m_size = 0,  // non-negative
        .m_slots = spam_module_slots,
    };

    PyMODINIT_FUNC
    PyInit_spam(void)
    {
        return PyModuleDef_Init(&spam_module);
    }

Note that the Python name for the exception object is `spam.error`. The `PyErr_NewException()` function may create a class with the base class being `Exception` (unless another class is passed in instead of `NULL`), described in Exceções embutidas.

Note also that the `SpamError` variable retains a reference to the newly created exception class; this is intentional! Since the exception could be removed from the module by external code, an owned reference to the class is needed to ensure that it will not be discarded, causing `SpamError` to become a dangling pointer. Should it become a dangling pointer, C code which raises the exception could cause a core dump or other unintended side effects.

For now, the `Py_DECREF()` call to remove this reference is missing. Even when the Python interpreter shuts down, the global `SpamError` variable will not be garbage-collected. It will “leak”. We did, however, ensure that this will happen at most once per process.

We discuss the use of `PyMODINIT_FUNC` as a function return type later in this sample.

The `spam.error` exception can be raised in your extension module using a call to `PyErr_SetString()` as shown below:

    static PyObject *
    spam_system(PyObject *self, PyObject *args)
    {
        const char *command;
        int sts;

        if (!PyArg_ParseTuple(args, "s", &command))
            return NULL;
        sts = system(command);
        if (sts < 0) {
            PyErr_SetString(SpamError, "System command failed");
            return NULL;
        }
        return PyLong_FromLong(sts);
    }

## 1.3. De volta ao exemplo

Going back to our example function, you should now be able to understand this statement:

    if (!PyArg_ParseTuple(args, "s", &command))
        return NULL;

It returns `NULL` (the error indicator for functions returning object pointers) if an error is detected in the argument list, relying on the exception set by `PyArg_ParseTuple()`. Otherwise the string value of the argument has been copied to the local variable `command`. This is a pointer assignment and you are not supposed to modify the string to which it points (so in Standard C, the variable `command` should properly be declared as `const char *command`).

The next statement is a call to the Unix function `system()`, passing it the string we just got from `PyArg_ParseTuple()`:

    sts = system(command);

Our `spam.system()` function must return the value of `sts` as a Python object. This is done using the function `PyLong_FromLong()`.

    return PyLong_FromLong(sts);

Neste caso, ele retornará um objeto inteiro (sim, até mesmo inteiros são objetos no heap em Python!)

If you have a C function that returns no useful argument (a function returning void), the corresponding Python function must return `None`. You need this idiom to do so (which is implemented by the `Py_RETURN_NONE` macro):

    Py_INCREF(Py_None);
    return Py_None;

`Py_None` is the C name for the special Python object `None`. It is a genuine Python object rather than a `NULL` pointer, which means “error” in most contexts, as we have seen.

## 1.4. A tabela de métodos e a função de inicialização do módulo

I promised to show how `spam_system()` is called from Python programs. First, we need to list its name and address in a “method table”:

    static PyMethodDef spam_methods[] = {
        ...
        {"system",  spam_system, METH_VARARGS,
         "Execute a shell command."},
        ...
        {NULL, NULL, 0, NULL}        /* Sentinel */
    };

Note the third entry (`METH_VARARGS`). This is a flag telling the interpreter the calling convention to be used for the C function. It should normally always be `METH_VARARGS` or `METH_VARARGS | METH_KEYWORDS`; a value of `0` means that an obsolete variant of `PyArg_ParseTuple()` is used.

When using only `METH_VARARGS`, the function should expect the Python-level parameters to be passed in as a tuple acceptable for parsing via `PyArg_ParseTuple()`; more information on this function is provided below.

The `METH_KEYWORDS` bit may be set in the third field if keyword arguments should be passed to the function. In this case, the C function should accept a third `PyObject *` parameter which will be a dictionary of keywords. Use `PyArg_ParseTupleAndKeywords()` to parse the arguments to such a function.

A tabela de métodos deve ser referenciada na estrutura de definição do módulo:

    static struct PyModuleDef spam_module = {
        ...
        .m_methods = spam_methods,
        ...
    };

This structure, in turn, must be passed to the interpreter in the module’s initialization function. The initialization function must be named `PyInit_name()`, where name is the name of the module, and should be the only non-`static` item defined in the module file:

    PyMODINIT_FUNC
    PyInit_spam(void)
    {
        return PyModuleDef_Init(&spam_module);
    }

Note that `PyMODINIT_FUNC` declares the function as `PyObject *` return type, declares any special linkage declarations required by the platform, and for C++ declares the function as `extern "C"`.

`PyInit_spam()` is called when each interpreter imports its module `spam` for the first time. (See below for comments about embedding Python.) A pointer to the module definition must be returned via `PyModuleDef_Init()`, so that the import machinery can create the module and store it in `sys.modules`.

When embedding Python, the `PyInit_spam()` function is not called automatically unless there’s an entry in the `PyImport_Inittab` table. To add the module to the initialization table, use `PyImport_AppendInittab()`, optionally followed by an import of the module:

    #define PY_SSIZE_T_CLEAN
    #include <Python.h>

    int
    main(int argc, char *argv[])
    {
        PyStatus status;
        PyConfig config;
        PyConfig_InitPythonConfig(&config);

        /* Add a built-in module, before Py_Initialize */
        if (PyImport_AppendInittab("spam", PyInit_spam) == -1) {
            fprintf(stderr, "Error: could not extend in-built modules table\n");
            exit(1);
        }

        /* Pass argv[0] to the Python interpreter */
        status = PyConfig_SetBytesString(&config, &config.program_name, argv[0]);
        if (PyStatus_Exception(status)) {
            goto exception;
        }

        /* Initialize the Python interpreter.  Required.
           If this step fails, it will be a fatal error. */
        status = Py_InitializeFromConfig(&config);
        if (PyStatus_Exception(status)) {
            goto exception;
        }
        PyConfig_Clear(&config);

        /* Optionally import the module; alternatively,
           import can be deferred until the embedded script
           imports it. */
        PyObject *pmodule = PyImport_ImportModule("spam");
        if (!pmodule) {
            PyErr_Print();
            fprintf(stderr, "Error: could not import module 'spam'\n");
        }

        // ... use Python C API here ...

        return 0;

      exception:
         PyConfig_Clear(&config);
         Py_ExitStatusException(status);
    }

Nota

If you declare a global variable or a local static one, the module may experience unintended side-effects on re-initialisation, for example when removing entries from `sys.modules` or importing compiled modules into multiple interpreters within a process (or following a `fork()` without an intervening `exec()`). If module state is not yet fully isolated, authors should consider marking the module as having no support for subinterpreters (via `Py_MOD_MULTIPLE_INTERPRETERS_NOT_SUPPORTED`).

A more substantial example module is included in the Python source distribution as `Modules/xxlimited.c`. This file may be used as a template or simply read as an example.

## 1.5. Compilação e vinculação

Há mais duas coisas a fazer antes de usar sua nova extensão: compilá-la e vinculá-la ao sistema Python. Se você usa carregamento dinâmico, os detalhes podem depender do estilo de carregamento dinâmico que seu sistema utiliza; consulte os capítulos sobre construção de módulos de extensão (capítulo Construindo extensões C e C++) e informações adicionais referentes apenas à construção no Windows (capítulo Construindo Extensões C e C++ no Windows) para obter mais informações sobre isso.

If you can’t use dynamic loading, or if you want to make your module a permanent part of the Python interpreter, you will have to change the configuration setup and rebuild the interpreter. Luckily, this is very simple on Unix: just place your file (`spammodule.c` for example) in the `Modules/` directory of an unpacked source distribution, add a line to the file `Modules/Setup.local` describing your file:

    spam spammodule.o

and rebuild the interpreter by running make in the toplevel directory. You can also run make in the `Modules/` subdirectory, but then you must first rebuild `Makefile` there by running ‘make Makefile’. (This is necessary each time you change the `Setup` file.)

If your module requires additional libraries to link with, these can be listed on the line in the configuration file as well, for instance:

    spam spammodule.o -lX11

## 1.6. Chamando funções Python de C

Até agora, nos concentramos em tornar funções C chamáveis a partir de Python. O inverso também é útil: chamar funções Python a partir de C. Isso é especialmente verdadeiro para bibliotecas que oferecem suporte às chamadas funções de “callback”. Se uma interface C utiliza callbacks, o Python equivalente frequentemente precisa fornecer um mecanismo de callback ao programador Python; a implementação exigirá a chamada das funções de callback Python a partir de um callback C. Outros usos também são imagináveis.

Felizmente, o interpretador Python é facilmente chamado recursivamente, e há uma interface padrão para chamar uma função Python. (Não vou me aprofundar em como chamar o analisador sintático do Python com uma string específica como entrada — se você estiver interessado, dê uma olhada na implementação da opção de linha de comando `-c` em `Modules/main.c` do código-fonte Python.)

Calling a Python function is easy. First, the Python program must somehow pass you the Python function object. You should provide a function (or some other interface) to do this. When this function is called, save a pointer to the Python function object (be careful to `Py_INCREF()` it!) in a global variable — or wherever you see fit. For example, the following function might be part of a module definition:

    static PyObject *my_callback = NULL;

    static PyObject *
    my_set_callback(PyObject *dummy, PyObject *args)
    {
        PyObject *result = NULL;
        PyObject *temp;

        if (PyArg_ParseTuple(args, "O:set_callback", &temp)) {
            if (!PyCallable_Check(temp)) {
                PyErr_SetString(PyExc_TypeError, "parameter must be callable");
                return NULL;
            }
            Py_XINCREF(temp);         /* Add a reference to new callback */
            Py_XDECREF(my_callback);  /* Dispose of previous callback */
            my_callback = temp;       /* Remember new callback */
            /* Boilerplate to return "None" */
            Py_INCREF(Py_None);
            result = Py_None;
        }
        return result;
    }

This function must be registered with the interpreter using the `METH_VARARGS` flag; this is described in section A tabela de métodos e a função de inicialização do módulo. The `PyArg_ParseTuple()` function and its arguments are documented in section Extraindo parâmetros em funções de extensão.

The macros `Py_XINCREF()` and `Py_XDECREF()` increment/decrement the reference count of an object and are safe in the presence of `NULL` pointers (but note that temp will not be `NULL` in this context). More info on them in section Contagens de referências.

Later, when it is time to call the function, you call the C function `PyObject_CallObject()`. This function has two arguments, both pointers to arbitrary Python objects: the Python function, and the argument list. The argument list must always be a tuple object, whose length is the number of arguments. To call the Python function with no arguments, pass in `NULL`, or an empty tuple; to call it with one argument, pass a singleton tuple. `Py_BuildValue()` returns a tuple when its format string consists of zero or more format codes between parentheses. For example:

    int arg;
    PyObject *arglist;
    PyObject *result;
    ...
    arg = 123;
    ...
    /* Time to call the callback */
    arglist = Py_BuildValue("(i)", arg);
    result = PyObject_CallObject(my_callback, arglist);
    Py_DECREF(arglist);

`PyObject_CallObject()` returns a Python object pointer: this is the return value of the Python function. `PyObject_CallObject()` is “reference-count-neutral” with respect to its arguments. In the example a new tuple was created to serve as the argument list, which is `Py_DECREF()`-ed immediately after the `PyObject_CallObject()` call.

The return value of `PyObject_CallObject()` is “new”: either it is a brand new object, or it is an existing object whose reference count has been incremented. So, unless you want to save it in a global variable, you should somehow `Py_DECREF()` the result, even (especially!) if you are not interested in its value.

Before you do this, however, it is important to check that the return value isn’t `NULL`. If it is, the Python function terminated by raising an exception. If the C code that called `PyObject_CallObject()` is called from Python, it should now return an error indication to its Python caller, so the interpreter can print a stack trace, or the calling Python code can handle the exception. If this is not possible or desirable, the exception should be cleared by calling `PyErr_Clear()`. For example:

    if (result == NULL)
        return NULL; /* Pass error back */
    ...use result...
    Py_DECREF(result);

Depending on the desired interface to the Python callback function, you may also have to provide an argument list to `PyObject_CallObject()`. In some cases the argument list is also provided by the Python program, through the same interface that specified the callback function. It can then be saved and used in the same manner as the function object. In other cases, you may have to construct a new tuple to pass as the argument list. The simplest way to do this is to call `Py_BuildValue()`. For example, if you want to pass an integral event code, you might use the following code:

    PyObject *arglist;
    ...
    arglist = Py_BuildValue("(l)", eventcode);
    result = PyObject_CallObject(my_callback, arglist);
    Py_DECREF(arglist);
    if (result == NULL)
        return NULL; /* Pass error back */
    /* Here maybe use the result */
    Py_DECREF(result);

Note the placement of `Py_DECREF(arglist)` immediately after the call, before the error check! Also note that strictly speaking this code is not complete: `Py_BuildValue()` may run out of memory, and this should be checked.

You may also call a function with keyword arguments by using `PyObject_Call()`, which supports arguments and keyword arguments. As in the above example, we use `Py_BuildValue()` to construct the dictionary.

    PyObject *dict;
    ...
    dict = Py_BuildValue("{s:i}", "name", val);
    result = PyObject_Call(my_callback, NULL, dict);
    Py_DECREF(dict);
    if (result == NULL)
        return NULL; /* Pass error back */
    /* Here maybe use the result */
    Py_DECREF(result);

## 1.7. Extraindo parâmetros em funções de extensão

The `PyArg_ParseTuple()` function is declared as follows:

    int PyArg_ParseTuple(PyObject *arg, const char *format, ...);

O argumento arg deve ser um objeto tupla contendo uma lista de argumentos passada de Python para uma função C. O argumento format deve ser uma string de formato, cuja sintaxe é explicada em Análise de argumentos e construção de valores no Manual de Referência da API C/Python. Os argumentos restantes devem ser endereços de variáveis cujo tipo é determinado pela string de formato.

Note that while `PyArg_ParseTuple()` checks that the Python arguments have the required types, it cannot check the validity of the addresses of C variables passed to the call: if you make mistakes there, your code will probably crash or at least overwrite random bits in memory. So be careful!

Note que quaisquer referências a objeto Python que são fornecidas ao chamador são referências emprestadas; não decremente a contagem de referências delas!

Alguns exemplos de chamadas:

    #define PY_SSIZE_T_CLEAN
    #include <Python.h>

    int ok;
    int i, j;
    long k, l;
    const char *s;
    Py_ssize_t size;

    ok = PyArg_ParseTuple(args, ""); /* No arguments */
        /* Python call: f() */

    ok = PyArg_ParseTuple(args, "s", &s); /* A string */
        /* Possible Python call: f('whoops!') */

    ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */
        /* Possible Python call: f(1, 2, 'three') */

    ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size);
        /* A pair of ints and a string, whose size is also returned */
        /* Possible Python call: f((1, 2), 'three') */

    {
        const char *file;
        const char *mode = "r";
        int bufsize = 0;
        ok = PyArg_ParseTuple(args, "s|si", &file, &mode, &bufsize);
        /* A string, and optionally another string and an integer */
        /* Possible Python calls:
           f('spam')
           f('spam', 'w')
           f('spam', 'wb', 100000) */
    }

    {
        int left, top, right, bottom, h, v;
        ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)",
                 &left, &top, &right, &bottom, &h, &v);
        /* A rectangle and a point */
        /* Possible Python call:
           f(((0, 0), (400, 300)), (10, 10)) */
    }

    {
        Py_complex c;
        ok = PyArg_ParseTuple(args, "D:myfunction", &c);
        /* a complex, also providing a function name for errors */
        /* Possible Python call: myfunction(1+2j) */
    }

## 1.8. Parâmetros nomeados para funções de extensão

The `PyArg_ParseTupleAndKeywords()` function is declared as follows:

    int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,
                                    const char *format, char * const *kwlist, ...);

The arg and format parameters are identical to those of the `PyArg_ParseTuple()` function. The kwdict parameter is the dictionary of keywords received as the third parameter from the Python runtime. The kwlist parameter is a `NULL`-terminated list of strings which identify the parameters; the names are matched with the type information from format from left to right. On success, `PyArg_ParseTupleAndKeywords()` returns true, otherwise it returns false and raises an appropriate exception.

Nota

Nested tuples cannot be parsed when using keyword arguments! Keyword parameters passed in which are not present in the kwlist will cause `TypeError` to be raised.

Aqui está um módulo de exemplo que usa parâmetros nomeados, baseado em um exemplo de Geoff Philbrick (philbrick@hks.com):

    #define PY_SSIZE_T_CLEAN
    #include <Python.h>

    static PyObject *
    keywdarg_parrot(PyObject *self, PyObject *args, PyObject *keywds)
    {
        int voltage;
        const char *state = "a stiff";
        const char *action = "voom";
        const char *type = "Norwegian Blue";

        static char *kwlist[] = {"voltage", "state", "action", "type", NULL};

        if (!PyArg_ParseTupleAndKeywords(args, keywds, "i|sss", kwlist,
                                         &voltage, &state, &action, &type))
            return NULL;

        printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",
               action, voltage);
        printf("-- Lovely plumage, the %s -- It's %s!\n", type, state);

        Py_RETURN_NONE;
    }

    static PyMethodDef keywdarg_methods[] = {
        /* The cast of the function is necessary since PyCFunction values
         * only take two PyObject* parameters, and keywdarg_parrot() takes
         * three.
         */
        {"parrot", (PyCFunction)(void(*)(void))keywdarg_parrot, METH_VARARGS | METH_KEYWORDS,
         "Print a lovely skit to standard output."},
        {NULL, NULL, 0, NULL}   /* sentinel */
    };

    static struct PyModuleDef keywdarg_module = {
        .m_base = PyModuleDef_HEAD_INIT,
        .m_name = "keywdarg",
        .m_size = 0,
        .m_methods = keywdarg_methods,
    };

    PyMODINIT_FUNC
    PyInit_keywdarg(void)
    {
        return PyModuleDef_Init(&keywdarg_module);
    }

## 1.9. Construindo valores arbitrários

This function is the counterpart to `PyArg_ParseTuple()`. It is declared as follows:

    PyObject *Py_BuildValue(const char *format, ...);

It recognizes a set of format units similar to the ones recognized by `PyArg_ParseTuple()`, but the arguments (which are input to the function, not output) must not be pointers, just values. It returns a new Python object, suitable for returning from a C function called from Python.

One difference with `PyArg_ParseTuple()`: while the latter requires its first argument to be a tuple (since Python argument lists are always represented as tuples internally), `Py_BuildValue()` does not always build a tuple. It builds a tuple only if its format string contains two or more format units. If the format string is empty, it returns `None`; if it contains exactly one format unit, it returns whatever object is described by that format unit. To force it to return a tuple of size 0 or one, parenthesize the format string.

Examples (to the left the call, to the right the resulting Python value):

    Py_BuildValue("")                        None
    Py_BuildValue("i", 123)                  123
    Py_BuildValue("iii", 123, 456, 789)      (123, 456, 789)
    Py_BuildValue("s", "hello")              'hello'
    Py_BuildValue("y", "hello")              b'hello'
    Py_BuildValue("ss", "hello", "world")    ('hello', 'world')
    Py_BuildValue("s#", "hello", 4)          'hell'
    Py_BuildValue("y#", "hello", 4)          b'hell'
    Py_BuildValue("()")                      ()
    Py_BuildValue("(i)", 123)                (123,)
    Py_BuildValue("(ii)", 123, 456)          (123, 456)
    Py_BuildValue("(i,i)", 123, 456)         (123, 456)
    Py_BuildValue("[i,i]", 123, 456)         [123, 456]
    Py_BuildValue("{s:i,s:i}",
                  "abc", 123, "def", 456)    {'abc': 123, 'def': 456}
    Py_BuildValue("((ii)(ii)) (ii)",
                  1, 2, 3, 4, 5, 6)          (((1, 2), (3, 4)), (5, 6))

## 1.10. Contagens de referências

In languages like C or C++, the programmer is responsible for dynamic allocation and deallocation of memory on the heap. In C, this is done using the functions `malloc()` and `free()`. In C++, the operators `new` and `delete` are used with essentially the same meaning and we’ll restrict the following discussion to the C case.

Every block of memory allocated with `malloc()` should eventually be returned to the pool of available memory by exactly one call to `free()`. It is important to call `free()` at the right time. If a block’s address is forgotten but `free()` is not called for it, the memory it occupies cannot be reused until the program terminates. This is called a memory leak. On the other hand, if a program calls `free()` for a block and then continues to use the block, it creates a conflict with reuse of the block through another `malloc()` call. This is called using freed memory. It has the same bad consequences as referencing uninitialized data — core dumps, wrong results, mysterious crashes.

Causas comuns de vazamentos de memória são caminhos incomuns no código. Por exemplo, uma função pode alocar um bloco de memória, executar algum cálculo e, em seguida, liberar o bloco novamente. Agora, uma alteração nos requisitos da função pode adicionar um teste ao cálculo que detecta uma condição de erro e pode retornar prematuramente da função. É fácil esquecer de liberar o bloco de memória alocado ao executar essa saída prematura, especialmente quando ela é adicionada posteriormente ao código. Esses vazamentos, uma vez introduzidos, geralmente passam despercebidos por um longo tempo: a saída de erro é executada apenas em uma pequena fração de todas as chamadas, e a maioria das máquinas modernas tem bastante memória virtual, portanto, o vazamento só se torna aparente em um processo de longa duração que usa a função com vazamento com frequência. Portanto, é importante evitar que vazamentos aconteçam por meio de uma convenção ou estratégia de codificação que minimize esse tipo de erro.

Since Python makes heavy use of `malloc()` and `free()`, it needs a strategy to avoid memory leaks as well as the use of freed memory. The chosen method is called reference counting. The principle is simple: every object contains a counter, which is incremented when a reference to the object is stored somewhere, and which is decremented when a reference to it is deleted. When the counter reaches zero, the last reference to the object has been deleted and the object is freed.

An alternative strategy is called automatic garbage collection. (Sometimes, reference counting is also referred to as a garbage collection strategy, hence my use of “automatic” to distinguish the two.) The big advantage of automatic garbage collection is that the user doesn’t need to call `free()` explicitly. (Another claimed advantage is an improvement in speed or memory usage — this is no hard fact however.) The disadvantage is that for C, there is no truly portable automatic garbage collector, while reference counting can be implemented portably (as long as the functions `malloc()` and `free()` are available — which the C Standard guarantees). Maybe some day a sufficiently portable automatic garbage collector will be available for C. Until then, we’ll have to live with reference counts.

Embora o Python utilize a implementação tradicional de contagem de referências, ele também oferece um detector de ciclos que funciona para detectar ciclos de referência. Isso permite que as aplicações não se preocupem em criar referências circulares diretas ou indiretas; esses são os pontos fracos da coleta de lixo implementada usando apenas a contagem de referências. Ciclos de referência consistem em objetos que contêm referências (possivelmente indiretas) a si mesmos, de modo que cada objeto no ciclo tem uma contagem de referências diferente de zero. Implementações típicas de contagem de referências não são capazes de recuperar a memória pertencente a nenhum objeto em um ciclo de referência, ou referenciada a partir dos objetos no ciclo, mesmo que não haja mais referências ao próprio ciclo.

The cycle detector is able to detect garbage cycles and can reclaim them. The `gc` module exposes a way to run the detector (the `collect()` function), as well as configuration interfaces and the ability to disable the detector at runtime.

### 1.10.1. Contagem de referências no Python

There are two macros, `Py_INCREF(x)` and `Py_DECREF(x)`, which handle the incrementing and decrementing of the reference count. `Py_DECREF()` also frees the object when the count reaches zero. For flexibility, it doesn’t call `free()` directly — rather, it makes a call through a function pointer in the object’s type object. For this purpose (and others), every object also contains a pointer to its type object.

The big question now remains: when to use `Py_INCREF(x)` and `Py_DECREF(x)`? Let’s first introduce some terms. Nobody “owns” an object; however, you can own a reference to an object. An object’s reference count is now defined as the number of owned references to it. The owner of a reference is responsible for calling `Py_DECREF()` when the reference is no longer needed. Ownership of a reference can be transferred. There are three ways to dispose of an owned reference: pass it on, store it, or call `Py_DECREF()`. Forgetting to dispose of an owned reference creates a memory leak.

It is also possible to borrow [2] a reference to an object. The borrower of a reference should not call `Py_DECREF()`. The borrower must not hold on to the object longer than the owner from which it was borrowed. Using a borrowed reference after the owner has disposed of it risks using freed memory and should be avoided completely [3].

The advantage of borrowing over owning a reference is that you don’t need to take care of disposing of the reference on all possible paths through the code — in other words, with a borrowed reference you don’t run the risk of leaking when a premature exit is taken. The disadvantage of borrowing over owning is that there are some subtle situations where in seemingly correct code a borrowed reference can be used after the owner from which it was borrowed has in fact disposed of it.

A borrowed reference can be changed into an owned reference by calling `Py_INCREF()`. This does not affect the status of the owner from which the reference was borrowed — it creates a new owned reference, and gives full owner responsibilities (the new owner must dispose of the reference properly, as well as the previous owner).

### 1.10.2. Regras de propriedade

Sempre que uma referência de objeto é passada para dentro ou para fora de uma função, faz parte da especificação da interface da função se a propriedade é transferida com a referência ou não.

Most functions that return a reference to an object pass on ownership with the reference. In particular, all functions whose function it is to create a new object, such as `PyLong_FromLong()` and `Py_BuildValue()`, pass ownership to the receiver. Even if the object is not actually new, you still receive ownership of a new reference to that object. For instance, `PyLong_FromLong()` maintains a cache of popular values and can return a reference to a cached item.

Many functions that extract objects from other objects also transfer ownership with the reference, for instance `PyObject_GetAttrString()`. The picture is less clear, here, however, since a few common routines are exceptions: `PyTuple_GetItem()`, `PyList_GetItem()`, `PyDict_GetItem()`, and `PyDict_GetItemString()` all return references that you borrow from the tuple, list or dictionary.

The function `PyImport_AddModule()` also returns a borrowed reference, even though it may actually create the object it returns: this is possible because an owned reference to the object is stored in `sys.modules`.

When you pass an object reference into another function, in general, the function borrows the reference from you — if it needs to store it, it will use `Py_INCREF()` to become an independent owner. There are exactly two important exceptions to this rule: `PyTuple_SetItem()` and `PyList_SetItem()`. These functions take over ownership of the item passed to them — even if they fail! (Note that `PyDict_SetItem()` and friends don’t take over ownership — they are “normal.”)

When a C function is called from Python, it borrows references to its arguments from the caller. The caller owns a reference to the object, so the borrowed reference’s lifetime is guaranteed until the function returns. Only when such a borrowed reference must be stored or passed on, it must be turned into an owned reference by calling `Py_INCREF()`.

A referência de objeto retornada de uma função C chamada do Python deve ser uma referência própria — a propriedade é transferida da função para seu chamador.

### 1.10.3. Gelo fino

There are a few situations where seemingly harmless use of a borrowed reference can lead to problems. These all have to do with implicit invocations of the interpreter, which can cause the owner of a reference to dispose of it.

The first and most important case to know about is using `Py_DECREF()` on an unrelated object while borrowing a reference to a list item. For instance:

    void
    bug(PyObject *list)
    {
        PyObject *item = PyList_GetItem(list, 0);

        PyList_SetItem(list, 1, PyLong_FromLong(0L));
        PyObject_Print(item, stdout, 0); /* BUG! */
    }

Esta função primeiro toma emprestada uma referência a `list[0]`, depois substitui `list[1]` pelo valor `0` e, por fim, imprime a referência emprestada. Parece inofensivo, certo? Mas não é!

Let’s follow the control flow into `PyList_SetItem()`. The list owns references to all its items, so when item 1 is replaced, it has to dispose of the original item 1. Now let’s suppose the original item 1 was an instance of a user-defined class, and let’s further suppose that the class defined a `__del__()` method. If this class instance has a reference count of 1, disposing of it will call its `__del__()` method. Internally, `PyList_SetItem()` calls `Py_DECREF()` on the replaced item, which invokes replaced item’s corresponding `tp_dealloc` function. During deallocation, `tp_dealloc` calls `tp_finalize`, which is mapped to the `__del__()` method for class instances (see PEP 442). This entire sequence happens synchronously within the `PyList_SetItem()` call.

Since it is written in Python, the `__del__()` method can execute arbitrary Python code. Could it perhaps do something to invalidate the reference to `item` in `bug()`? You bet! Assuming that the list passed into `bug()` is accessible to the `__del__()` method, it could execute a statement to the effect of `del list[0]`, and assuming this was the last reference to that object, it would free the memory associated with it, thereby invalidating `item`.

A solução, depois de descobrir a origem do problema, é fácil: incrementar temporariamente a contagem de referências. A versão correta da função é:

    void
    no_bug(PyObject *list)
    {
        PyObject *item = PyList_GetItem(list, 0);

        Py_INCREF(item);
        PyList_SetItem(list, 1, PyLong_FromLong(0L));
        PyObject_Print(item, stdout, 0);
        Py_DECREF(item);
    }

This is a true story. An older version of Python contained variants of this bug and someone spent a considerable amount of time in a C debugger to figure out why his `__del__()` methods would fail…

The second case of problems with a borrowed reference is a variant involving threads. Normally, multiple threads in the Python interpreter can’t get in each other’s way, because there is a global lock protecting Python’s entire object space. However, it is possible to temporarily release this lock using the macro `Py_BEGIN_ALLOW_THREADS`, and to re-acquire it using `Py_END_ALLOW_THREADS`. This is common around blocking I/O calls, to let other threads use the processor while waiting for the I/O to complete. Obviously, the following function has the same problem as the previous one:

    void
    bug(PyObject *list)
    {
        PyObject *item = PyList_GetItem(list, 0);
        Py_BEGIN_ALLOW_THREADS
        ...some blocking I/O call...
        Py_END_ALLOW_THREADS
        PyObject_Print(item, stdout, 0); /* BUG! */
    }

### 1.10.4. Ponteiros NULL

In general, functions that take object references as arguments do not expect you to pass them `NULL` pointers, and will dump core (or cause later core dumps) if you do so. Functions that return object references generally return `NULL` only to indicate that an exception occurred. The reason for not testing for `NULL` arguments is that functions often pass the objects they receive on to other function — if each function were to test for `NULL`, there would be a lot of redundant tests and the code would run more slowly.

It is better to test for `NULL` only at the “source:” when a pointer that may be `NULL` is received, for example, from `malloc()` or from a function that may raise an exception.

The macros `Py_INCREF()` and `Py_DECREF()` do not check for `NULL` pointers — however, their variants `Py_XINCREF()` and `Py_XDECREF()` do.

The macros for checking for a particular object type (`Pytype_Check()`) don’t check for `NULL` pointers — again, there is much code that calls several of these in a row to test an object against various different expected types, and this would generate redundant tests. There are no variants with `NULL` checking.

The C function calling mechanism guarantees that the argument list passed to C functions (`args` in the examples) is never `NULL` — in fact it guarantees that it is always a tuple [4].

It is a severe error to ever let a `NULL` pointer “escape” to the Python user.

## 1.11. Escrevendo extensões em C++

É possível escrever módulos de extensão em C++. Algumas restrições se aplicam. Se o programa principal (o interpretador Python) for compilado e vinculado pelo compilador C, objetos globais ou estáticos com construtores não poderão ser usados. Isso não é um problema se o programa principal for vinculado pelo compilador C++. Funções que serão chamadas pelo interpretador Python (em particular, funções de inicialização de módulos) devem ser declaradas usando `extern "C"`. Não é necessário incluir os arquivos de cabeçalho Python entre `extern "C" {...}` — eles já usam esta forma se o símbolo `__cplusplus` estiver definido (todos os compiladores C++ recentes definem este símbolo).

## 1.12. Fornecendo uma API C para um módulo de extensão

Muitos módulos de extensão apenas fornecem novas funções e tipos para serem usados em Python, mas às vezes o código em um módulo de extensão pode ser útil para outros módulos de extensão. Por exemplo, um módulo de extensão poderia implementar um tipo “collection”, que funciona como listas sem ordem. Assim como o tipo de lista padrão do Python possui uma API C que permite que os módulos de extensão criem e manipulem listas, esse novo tipo de coleção deve ter um conjunto de funções em C para manipulação direta de outros módulos de extensão.

À primeira vista, isso parece fácil: basta escrever as funções (sem declará-las `static`, é claro), fornecer um arquivo de cabeçalho apropriado e documentar a API C. E, de fato, isso funcionaria se todos os módulos de extensão estivessem sempre vinculados estaticamente ao interpretador Python. Quando os módulos são usados como bibliotecas compartilhadas, no entanto, os símbolos definidos em um módulo podem não ser visíveis para outro módulo. Os detalhes de visibilidade dependem do sistema operacional; alguns sistemas usam um espaço de nomes global para o interpretador Python e todos os módulos de extensão (Windows, por exemplo), enquanto outros exigem uma lista explícita de símbolos importados no momento da vinculação do módulo (AIX é um exemplo) ou oferecem uma escolha de estratégias diferentes (a maioria dos Unices). E mesmo que os símbolos sejam globalmente visíveis, o módulo cujas funções se deseja chamar pode não ter sido carregado ainda!

A portabilidade, portanto, exige que não se façam suposições sobre a visibilidade dos símbolos. Isso significa que todos os símbolos em módulos de extensão devem ser declarados `static`, exceto a função de inicialização do módulo, a fim de evitar conflitos de nomes com outros módulos de extensão (conforme discutido na seção A tabela de métodos e a função de inicialização do módulo). E significa que os símbolos que deveriam ser acessíveis a partir de outros módulos de extensão devem ser exportados de uma maneira diferente.

Python provides a special mechanism to pass C-level information (pointers) from one extension module to another one: Capsules. A Capsule is a Python data type which stores a pointer (void*). Capsules can only be created and accessed via their C API, but they can be passed around like any other Python object. In particular, they can be assigned to a name in an extension module’s namespace. Other extension modules can then import this module, retrieve the value of this name, and then retrieve the pointer from the Capsule.

Existem muitas maneiras de usar Cápsulas para exportar a API C de um módulo de extensão. Cada função pode obter sua própria Cápsula, ou todos os ponteiros da API C podem ser armazenados em um array cujo endereço é publicado em uma Cápsula. E as diversas tarefas de armazenamento e recuperação dos ponteiros podem ser distribuídas de diferentes maneiras entre o módulo que fornece o código e os módulos clientes.

Whichever method you choose, it’s important to name your Capsules properly. The function `PyCapsule_New()` takes a name parameter (const char*); you’re permitted to pass in a `NULL` name, but we strongly encourage you to specify a name. Properly named Capsules provide a degree of runtime type-safety; there is no feasible way to tell one unnamed Capsule from another.

Em particular, as Cápsulas usadas para expor APIs C devem receber um nome seguindo esta convenção:

    modulename.attributename

The convenience function `PyCapsule_Import()` makes it easy to load a C API provided via a Capsule, but only if the Capsule’s name matches this convention. This behavior gives C API users a high degree of certainty that the Capsule they load contains the correct C API.

The following example demonstrates an approach that puts most of the burden on the writer of the exporting module, which is appropriate for commonly used library modules. It stores all C API pointers (just one in the example!) in an array of void pointers which becomes the value of a Capsule. The header file corresponding to the module provides a macro that takes care of importing the module and retrieving its C API pointers; client modules only have to call this macro before accessing the C API.

The exporting module is a modification of the `spam` module from section Um Exemplo Simples. The function `spam.system()` does not call the C library function `system()` directly, but a function `PySpam_System()`, which would of course do something more complicated in reality (such as adding “spam” to every command). This function `PySpam_System()` is also exported to other extension modules.

The function `PySpam_System()` is a plain C function, declared `static` like everything else:

    static int
    PySpam_System(const char *command)
    {
        return system(command);
    }

The function `spam_system()` is modified in a trivial way:

    static PyObject *
    spam_system(PyObject *self, PyObject *args)
    {
        const char *command;
        int sts;

        if (!PyArg_ParseTuple(args, "s", &command))
            return NULL;
        sts = PySpam_System(command);
        return PyLong_FromLong(sts);
    }

No início do módulo, logo após a linha

    #include <Python.h>

mais duas linhas devem ser adicionadas:

    #define SPAM_MODULE
    #include "spammodule.h"

The `#define` is used to tell the header file that it is being included in the exporting module, not a client module. Finally, the module’s `mod_exec` function must take care of initializing the C API pointer array:

    static int
    spam_module_exec(PyObject *m)
    {
        static void *PySpam_API[PySpam_API_pointers];
        PyObject *c_api_object;

        /* Initialize the C API pointer array */
        PySpam_API[PySpam_System_NUM] = (void *)PySpam_System;

        /* Create a Capsule containing the API pointer array's address */
        c_api_object = PyCapsule_New((void *)PySpam_API, "spam._C_API", NULL);

        if (PyModule_Add(m, "_C_API", c_api_object) < 0) {
            return -1;
        }

        return 0;
    }

Note that `PySpam_API` is declared `static`; otherwise the pointer array would disappear when `PyInit_spam()` terminates!

A maior parte do trabalho está no arquivo de cabeçalho `spammodule.h`, que se parece com isso:

    #ifndef Py_SPAMMODULE_H
    #define Py_SPAMMODULE_H
    #ifdef __cplusplus
    extern "C" {
    #endif

    /* Header file for spammodule */

    /* C API functions */
    #define PySpam_System_NUM 0
    #define PySpam_System_RETURN int
    #define PySpam_System_PROTO (const char *command)

    /* Total number of C API pointers */
    #define PySpam_API_pointers 1

    #ifdef SPAM_MODULE
    /* This section is used when compiling spammodule.c */

    static PySpam_System_RETURN PySpam_System PySpam_System_PROTO;

    #else
    /* This section is used in modules that use spammodule's API */

    static void **PySpam_API;

    #define PySpam_System \
     (*(PySpam_System_RETURN (*)PySpam_System_PROTO) PySpam_API[PySpam_System_NUM])

    /* Return -1 on error, 0 on success.
     * PyCapsule_Import will set an exception if there's an error.
     */
    static int
    import_spam(void)
    {
        PySpam_API = (void **)PyCapsule_Import("spam._C_API", 0);
        return (PySpam_API != NULL) ? 0 : -1;
    }

    #endif

    #ifdef __cplusplus
    }
    #endif

    #endif /* !defined(Py_SPAMMODULE_H) */

All that a client module must do in order to have access to the function `PySpam_System()` is to call the function (or rather macro) `import_spam()` in its `mod_exec` function:

    static int
    client_module_exec(PyObject *m)
    {
        if (import_spam() < 0) {
            return -1;
        }
        /* additional initialization can happen here */
        return 0;
    }

The main disadvantage of this approach is that the file `spammodule.h` is rather complicated. However, the basic structure is the same for each function that is exported, so it has to be learned only once.

Por fim, vale mencionar que as Cápsulas oferecem funcionalidades adicionais, especialmente úteis para alocação e desalocação de memória do ponteiro armazenado em uma Cápsula. Os detalhes são descritos no Manual de Referência da API Python/C, na seção Capsules e na implementação das Cápsulas (arquivos `Include/pycapsule.h` e `Objects/pycapsule.c` na distribuição do código-fonte Python).

Notas de rodapé

[ 1 ]

Uma interface para esta função já existe no módulo padrão `os` — ela foi escolhida como um exemplo simples e direto.

[ 2 ]

A metáfora de “pegar emprestado” uma referência não é completamente correta: o proprietário ainda tem uma cópia da referência.

[ 3 ]

Checking that the reference count is at least 1 does not work — the reference count itself could be in freed memory and may thus be reused for another object!

[ 4 ]

These guarantees don’t hold when you use the “old” style calling convention — this is still found in much existing code.