OpenVINO™ Extension API allows you to register custom operations to support models with operations which OpenVINO™ does not support out-of-the-box. This capability requires writing code in C++, so if you are using Python to develop your application you need to build a separate shared library implemented in C++ first and load it in Python using ``add_extension`` API. Please refer to :ref:`Create library with extensions <create_library_with_extensions>` for more details on library creation and usage. The remining part of this document describes how to implement an operation class.
To add your custom operation, create a new class that extends ``ov::Op``, which is in turn derived from ``:ref:`ov::Node <doxid-classov_1_1_node>```, the base class for all graph operations in OpenVINO™. To add ``ov::Op``, include the next file:
1. Add the ``OPENVINO_OP`` macro which defines a ``NodeTypeInfo`` object that identifies the type of the operation to the graph users and helps with dynamic type resolution. The type info of an operation currently consists of a string operation identifier and a string for operation version.
3. Override the shape inference method ``validate_and_infer_types``. This method is called multiple times during graph manipulations to determine the shapes and element types of the operations outputs. To access the input shapes and input element types, use the ``get_input_partial_shape()`` and ``get_input_element_type()`` methods of ``:ref:`ov::Node <doxid-classov_1_1_node>```. Set the inferred shape and element type of the output using ``set_output_type``.
4. Override the ``clone_with_new_inputs`` method, which enables graph manipulation routines to create copies of this operation and connect it to different nodes during optimization.
5. Override the ``visit_attributes`` method, which enables serialization and deserialization of operation attributes. An ``AttributeVisitor`` is passed to the method, and the implementation is expected to walk over all the attributes in the op using the type-aware ``on_attribute`` helper. Helpers are already implemented for standard C++ types like ``int64_t``, ``float``, ``bool``, ``vector``, and for existing OpenVINO defined types.
6. Override ``evaluate``, which is an optional method that enables fallback of some devices to this implementation and the application of constant folding if there is a custom operation on the constant branch. If your operation contains ``evaluate`` method you also need to override the ``has_evaluate`` method, this method allows to get information about availability of ``evaluate`` method for the operation.
``:ref:`ov::Node::validate_and_infer_types <doxid-classov_1_1_node_1ac5224b5be848ec670d2078d9816d12e7>``` method validates operation attributes and calculates output shapes using attributes of the operation.
``:ref:`ov::Node::clone_with_new_inputs <doxid-classov_1_1_node_1a04cb103fa069c3b7944ab7c44d94f5ff>``` method creates a copy of the operation with new inputs.
``:ref:`ov::Node::visit_attributes <doxid-classov_1_1_node_1a9743b56d352970486d17dae2416d958e>``` method enables you to visit all operation attributes.
``:ref:`ov::Node::evaluate <doxid-classov_1_1_node_1acfb82acc8349d7138aeaa05217c7014e>``` method enables you to apply constant folding to an operation.
