It is inspired from the :epkg:`reverse Polish notation`. Following example implements the euclidean distance. This API tries to keep it simple and intuitive to short functions.

.. runpython::
    :showcode:

    import numpy as np
    from onnx_array_api.light_api import start
    from onnx_array_api.plotting.text_plot import onnx_simple_text_plot

    model = (
        start()
        .vin("X")
        .vin("Y")
        .bring("X", "Y")
        .Sub()
        .rename("dxy")
        .cst(np.array([2], dtype=np.int64), "two")
        .bring("dxy", "two")
        .Pow()
        .ReduceSum()
        .rename("Z")
        .vout()
        .to_onnx()
    )

    print(onnx_simple_text_plot(model))

There are two kinds of methods, the graph methods, playing with the graph structure, and the methods for operators starting with an upper letter.

Any graph must start with function :func:`start <onnx_array_api.light_api.start>`. It is usually following by vin to add an input.

• bring (:meth:`Var.bring <onnx_array_api.light_api.Var.bring>`, :meth:`Vars.bring <onnx_array_api.light_api.Vars.bring>`): assembles multiple results into a set before calling an operator taking mulitple inputs,
• cst (:meth:`Var.cst <onnx_array_api.light_api.Var.cst>`, :meth:`Vars.cst <onnx_array_api.light_api.Vars.cst>`): adds a constant tensor to the graph,
• rename (:meth:`Var.rename <onnx_array_api.light_api.Var.rename>`, :meth:`Vars.rename <onnx_array_api.light_api.Vars.rename>`): renames or give a name to a variable in order to call it later.
• vin (:meth:`Var.vin <onnx_array_api.light_api.Var.vin>`, :meth:`Vars.vin <onnx_array_api.light_api.Vars.vin>`): adds an input to the graph,
• vout (:meth:`Var.vout <onnx_array_api.light_api.Var.vout>`, :meth:`Vars.vout <onnx_array_api.light_api.Vars.vout>`): declares an existing result as an output.

These methods are implemented in class :class:`onnx_array_api.light_api.var.BaseVar`

They are described in :epkg:`ONNX Operators` and redefined in a stable API so that the definition should not change depending on this opset. :class:`onnx_array_api.light_api.Var` defines all operators taking only one input. :class:`onnx_array_api.light_api.Vars` defines all other operators.

Numpy users expect methods such as reshape, property shape or operator + to be available as well and that the case. They are defined in class :class:`Var <onnx_array_api.light_api.Var>` or :class:`Vars <onnx_array_api.light_api.Vars>` depending on the number of inputs they require. Their name starts with a lower letter.

The following example uses operator Normalizer from domain ai.onnx.ml. The operator name is called with the syntax <domain>.<operator name>. The domain may have dots in its name but it must follow the python definition of a variable. The operator Normalizer becomes ai.onnx.ml.Normalizer.

.. runpython::
    :showcode:

    import numpy as np
    from onnx_array_api.light_api import start
    from onnx_array_api.plotting.text_plot import onnx_simple_text_plot

    model = (
        start(opset=19, opsets={"ai.onnx.ml": 3})
        .vin("X")
        .reshape((-1, 1))
        .rename("USE")
        .ai.onnx.ml.Normalizer(norm="MAX")
        .rename("Y")
        .vout()
        .to_onnx()
    )

    print(onnx_simple_text_plot(model))