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311 lines
15 KiB
311 lines
15 KiB
Functions
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#########
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Before proceeding with this section, make sure that you are already familiar
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with the basics of binding functions and classes, as explained in :doc:`/basics`
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and :doc:`/classes`. The following guide is applicable to both free and member
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functions, i.e. *methods* in Python.
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Return value policies
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=====================
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Python and C++ use fundamentally different ways of managing the memory and
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lifetime of objects managed by them. This can lead to issues when creating
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bindings for functions that return a non-trivial type. Just by looking at the
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type information, it is not clear whether Python should take charge of the
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returned value and eventually free its resources, or if this is handled on the
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C++ side. For this reason, pybind11 provides a several `return value policy`
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annotations that can be passed to the :func:`module::def` and
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:func:`class_::def` functions. The default policy is
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:enum:`return_value_policy::automatic`.
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Return value policies are tricky, and it's very important to get them right.
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Just to illustrate what can go wrong, consider the following simple example:
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.. code-block:: cpp
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/* Function declaration */
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Data *get_data() { return _data; /* (pointer to a static data structure) */ }
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...
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/* Binding code */
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m.def("get_data", &get_data); // <-- KABOOM, will cause crash when called from Python
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What's going on here? When ``get_data()`` is called from Python, the return
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value (a native C++ type) must be wrapped to turn it into a usable Python type.
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In this case, the default return value policy (:enum:`return_value_policy::automatic`)
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causes pybind11 to assume ownership of the static ``_data`` instance.
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When Python's garbage collector eventually deletes the Python
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wrapper, pybind11 will also attempt to delete the C++ instance (via ``operator
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delete()``) due to the implied ownership. At this point, the entire application
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will come crashing down, though errors could also be more subtle and involve
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silent data corruption.
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In the above example, the policy :enum:`return_value_policy::reference` should have
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been specified so that the global data instance is only *referenced* without any
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implied transfer of ownership, i.e.:
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.. code-block:: cpp
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m.def("get_data", &get_data, return_value_policy::reference);
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On the other hand, this is not the right policy for many other situations,
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where ignoring ownership could lead to resource leaks.
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As a developer using pybind11, it's important to be familiar with the different
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return value policies, including which situation calls for which one of them.
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The following table provides an overview of available policies:
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.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
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+--------------------------------------------------+----------------------------------------------------------------------------+
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| Return value policy | Description |
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+==================================================+============================================================================+
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| :enum:`return_value_policy::take_ownership` | Reference an existing object (i.e. do not create a new copy) and take |
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| | ownership. Python will call the destructor and delete operator when the |
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| | object's reference count reaches zero. Undefined behavior ensues when the |
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| | C++ side does the same, or when the data was not dynamically allocated. |
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+--------------------------------------------------+----------------------------------------------------------------------------+
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| :enum:`return_value_policy::copy` | Create a new copy of the returned object, which will be owned by Python. |
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| | This policy is comparably safe because the lifetimes of the two instances |
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| | are decoupled. |
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+--------------------------------------------------+----------------------------------------------------------------------------+
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| :enum:`return_value_policy::move` | Use ``std::move`` to move the return value contents into a new instance |
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| | that will be owned by Python. This policy is comparably safe because the |
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| | lifetimes of the two instances (move source and destination) are decoupled.|
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+--------------------------------------------------+----------------------------------------------------------------------------+
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| :enum:`return_value_policy::reference` | Reference an existing object, but do not take ownership. The C++ side is |
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| | responsible for managing the object's lifetime and deallocating it when |
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| | it is no longer used. Warning: undefined behavior will ensue when the C++ |
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| | side deletes an object that is still referenced and used by Python. |
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+--------------------------------------------------+----------------------------------------------------------------------------+
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| :enum:`return_value_policy::reference_internal` | Indicates that the lifetime of the return value is tied to the lifetime |
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| | of a parent object, namely the implicit ``this``, or ``self`` argument of |
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| | the called method or property. Internally, this policy works just like |
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| | :enum:`return_value_policy::reference` but additionally applies a |
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| | ``keep_alive<0, 1>`` *call policy* (described in the next section) that |
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| | prevents the parent object from being garbage collected as long as the |
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| | return value is referenced by Python. This is the default policy for |
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| | property getters created via ``def_property``, ``def_readwrite``, etc. |
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+--------------------------------------------------+----------------------------------------------------------------------------+
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| :enum:`return_value_policy::automatic` | This is the default return value policy, which falls back to the policy |
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| | :enum:`return_value_policy::take_ownership` when the return value is a |
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| | pointer. Otherwise, it uses :enum:`return_value::move` or |
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| | :enum:`return_value::copy` for rvalue and lvalue references, respectively. |
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| | See above for a description of what all of these different policies do. |
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+--------------------------------------------------+----------------------------------------------------------------------------+
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| :enum:`return_value_policy::automatic_reference` | As above, but use policy :enum:`return_value_policy::reference` when the |
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| | return value is a pointer. This is the default conversion policy for |
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| | function arguments when calling Python functions manually from C++ code |
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| | (i.e. via handle::operator()). You probably won't need to use this. |
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+--------------------------------------------------+----------------------------------------------------------------------------+
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Return value policies can also be applied to properties:
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.. code-block:: cpp
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class_<MyClass>(m, "MyClass")
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.def_property("data", &MyClass::getData, &MyClass::setData,
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py::return_value_policy::copy);
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Technically, the code above applies the policy to both the getter and the
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setter function, however, the setter doesn't really care about *return*
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value policies which makes this a convenient terse syntax. Alternatively,
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targeted arguments can be passed through the :class:`cpp_function` constructor:
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.. code-block:: cpp
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class_<MyClass>(m, "MyClass")
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.def_property("data"
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py::cpp_function(&MyClass::getData, py::return_value_policy::copy),
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py::cpp_function(&MyClass::setData)
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);
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.. warning::
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Code with invalid return value policies might access unitialized memory or
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free data structures multiple times, which can lead to hard-to-debug
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non-determinism and segmentation faults, hence it is worth spending the
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time to understand all the different options in the table above.
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.. note::
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One important aspect of the above policies is that they only apply to
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instances which pybind11 has *not* seen before, in which case the policy
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clarifies essential questions about the return value's lifetime and
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ownership. When pybind11 knows the instance already (as identified by its
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type and address in memory), it will return the existing Python object
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wrapper rather than creating a new copy.
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.. note::
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The next section on :ref:`call_policies` discusses *call policies* that can be
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specified *in addition* to a return value policy from the list above. Call
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policies indicate reference relationships that can involve both return values
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and parameters of functions.
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.. note::
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As an alternative to elaborate call policies and lifetime management logic,
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consider using smart pointers (see the section on :ref:`smart_pointers` for
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details). Smart pointers can tell whether an object is still referenced from
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C++ or Python, which generally eliminates the kinds of inconsistencies that
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can lead to crashes or undefined behavior. For functions returning smart
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pointers, it is not necessary to specify a return value policy.
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.. _call_policies:
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Additional call policies
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========================
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In addition to the above return value policies, further `call policies` can be
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specified to indicate dependencies between parameters. There is currently just
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one policy named ``keep_alive<Nurse, Patient>``, which indicates that the
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argument with index ``Patient`` should be kept alive at least until the
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argument with index ``Nurse`` is freed by the garbage collector. Argument
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indices start at one, while zero refers to the return value. For methods, index
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``1`` refers to the implicit ``this`` pointer, while regular arguments begin at
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index ``2``. Arbitrarily many call policies can be specified. When a ``Nurse``
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with value ``None`` is detected at runtime, the call policy does nothing.
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This feature internally relies on the ability to create a *weak reference* to
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the nurse object, which is permitted by all classes exposed via pybind11. When
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the nurse object does not support weak references, an exception will be thrown.
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Consider the following example: here, the binding code for a list append
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operation ties the lifetime of the newly added element to the underlying
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container:
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.. code-block:: cpp
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py::class_<List>(m, "List")
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.def("append", &List::append, py::keep_alive<1, 2>());
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.. note::
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``keep_alive`` is analogous to the ``with_custodian_and_ward`` (if Nurse,
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Patient != 0) and ``with_custodian_and_ward_postcall`` (if Nurse/Patient ==
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0) policies from Boost.Python.
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.. seealso::
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The file :file:`tests/test_keep_alive.cpp` contains a complete example
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that demonstrates using :class:`keep_alive` in more detail.
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.. _python_objects_as_args:
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Python objects as arguments
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===========================
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pybind11 exposes all major Python types using thin C++ wrapper classes. These
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wrapper classes can also be used as parameters of functions in bindings, which
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makes it possible to directly work with native Python types on the C++ side.
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For instance, the following statement iterates over a Python ``dict``:
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.. code-block:: cpp
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void print_dict(py::dict dict) {
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/* Easily interact with Python types */
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for (auto item : dict)
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std::cout << "key=" << item.first << ", "
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<< "value=" << item.second << std::endl;
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}
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It can be exported:
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.. code-block:: cpp
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m.def("print_dict", &print_dict);
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And used in Python as usual:
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.. code-block:: pycon
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>>> print_dict({'foo': 123, 'bar': 'hello'})
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key=foo, value=123
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key=bar, value=hello
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For more information on using Python objects in C++, see :doc:`/advanced/pycpp/index`.
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Accepting \*args and \*\*kwargs
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===============================
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Python provides a useful mechanism to define functions that accept arbitrary
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numbers of arguments and keyword arguments:
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.. code-block:: python
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def generic(*args, **kwargs):
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... # do something with args and kwargs
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Such functions can also be created using pybind11:
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.. code-block:: cpp
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void generic(py::args args, py::kwargs kwargs) {
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/// .. do something with args
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if (kwargs)
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/// .. do something with kwargs
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}
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/// Binding code
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m.def("generic", &generic);
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The class ``py::args`` derives from ``py::tuple`` and ``py::kwargs`` derives
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from ``py::dict``. Note that the ``kwargs`` argument is invalid if no keyword
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arguments were actually provided. Please refer to the other examples for
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details on how to iterate over these, and on how to cast their entries into
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C++ objects. A demonstration is also available in
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``tests/test_kwargs_and_defaults.cpp``.
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.. warning::
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Unlike Python, pybind11 does not allow combining normal parameters with the
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``args`` / ``kwargs`` special parameters.
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Default arguments revisited
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===========================
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The section on :ref:`default_args` previously discussed basic usage of default
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arguments using pybind11. One noteworthy aspect of their implementation is that
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default arguments are converted to Python objects right at declaration time.
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Consider the following example:
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.. code-block:: cpp
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py::class_<MyClass>("MyClass")
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.def("myFunction", py::arg("arg") = SomeType(123));
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In this case, pybind11 must already be set up to deal with values of the type
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``SomeType`` (via a prior instantiation of ``py::class_<SomeType>``), or an
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exception will be thrown.
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Another aspect worth highlighting is that the "preview" of the default argument
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in the function signature is generated using the object's ``__repr__`` method.
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If not available, the signature may not be very helpful, e.g.:
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.. code-block:: pycon
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FUNCTIONS
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...
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| myFunction(...)
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| Signature : (MyClass, arg : SomeType = <SomeType object at 0x101b7b080>) -> NoneType
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...
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The first way of addressing this is by defining ``SomeType.__repr__``.
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Alternatively, it is possible to specify the human-readable preview of the
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default argument manually using the ``arg_v`` notation:
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.. code-block:: cpp
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py::class_<MyClass>("MyClass")
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.def("myFunction", py::arg_v("arg", SomeType(123), "SomeType(123)"));
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Sometimes it may be necessary to pass a null pointer value as a default
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argument. In this case, remember to cast it to the underlying type in question,
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like so:
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.. code-block:: cpp
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py::class_<MyClass>("MyClass")
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.def("myFunction", py::arg("arg") = (SomeType *) nullptr);
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