# ONNX with Python

Next sections highlight the main functions used to build
an ONNX graph with the {ref}`Python API <l-python-onnx-api>`
*onnx* offers.

(l-onnx-linear-regression-onnx-api)=

## A simple example: a linear regression

The linear regression is the most simple model
in machine learning described by the following expression
$Y = XA + B$. We can see it as a function of three
variables $Y = f(X, A, B)$ decomposed into
`y = Add(MatMul(X, A), B)`. That what's we need to represent
with ONNX operators. The first thing is to implement a function
with {ref}`ONNX operators <l-onnx-operators>`.
ONNX is strongly typed. Shape and type must be defined for both
input and output of the function. That said, we need four functions
to build the graph among the {ref}`l-onnx-make-function`:

- `make_tensor_value_info`: declares a variable (input or output)
  given its shape and type
- `make_node`: creates a node defined by an operation
  (an operator type), its inputs and outputs
- `make_graph`: a function to create an ONNX graph with
  the objects created by the two previous functions
- `make_model`: a last function which merges the graph and
  additional metadata

All along the creation, we need to give a name to every input,
output of every node of the graph. Input and output of the graph
are defined by onnx objects, strings are used to refer to
intermediate results. This is how it looks like.

```{eval-rst}
.. exec_code::

    # imports

    from onnx import TensorProto
    from onnx.helper import (
        make_model, make_node, make_graph,
        make_tensor_value_info)
    from onnx.checker import check_model

    # inputs

    # 'X' is the name, TensorProto.FLOAT the type, [None, None] the shape
    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])

    # outputs, the shape is left undefined

    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])

    # nodes

    # It creates a node defined by the operator type MatMul,
    # 'X', 'A' are the inputs of the node, 'XA' the output.
    node1 = make_node('MatMul', ['X', 'A'], ['XA'])
    node2 = make_node('Add', ['XA', 'B'], ['Y'])

    # from nodes to graph
    # the graph is built from the list of nodes, the list of inputs,
    # the list of outputs and a name.

    graph = make_graph([node1, node2],  # nodes
                        'lr',  # a name
                        [X, A, B],  # inputs
                        [Y])  # outputs

    # onnx graph
    # there is no metadata in this case.

    onnx_model = make_model(graph)

    # Let's check the model is consistent,
    # this function is described in section
    # Checker and Shape Inference.
    check_model(onnx_model)

    # the work is done, let's display it...
    print(onnx_model)
```

```{image} images/dot_linreg.png
```

An empty shape (`None`) means any shape, a shape defined as `[None, None]`
tells this object is a tensor with two dimensions without any further precision.
The ONNX graph can also be inspected by looking into the fields
of each object of the graph.

```{eval-rst}
.. exec_code::

    from onnx import TensorProto
    from onnx.helper import (
        make_model, make_node, make_graph,
        make_tensor_value_info)
    from onnx.checker import check_model

    def shape2tuple(shape):
        return tuple(getattr(d, 'dim_value', 0) for d in shape.dim)

    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])
    node1 = make_node('MatMul', ['X', 'A'], ['XA'])
    node2 = make_node('Add', ['XA', 'B'], ['Y'])
    graph = make_graph([node1, node2], 'lr', [X, A, B], [Y])
    onnx_model = make_model(graph)
    check_model(onnx_model)

    # the list of inputs
    print('** inputs **')
    print(onnx_model.graph.input)

    # in a more nicely format
    print('** inputs **')
    for obj in onnx_model.graph.input:
        print("name=%r dtype=%r shape=%r" % (
            obj.name, obj.type.tensor_type.elem_type,
            shape2tuple(obj.type.tensor_type.shape)))

    # the list of outputs
    print('** outputs **')
    print(onnx_model.graph.output)

    # in a more nicely format
    print('** outputs **')
    for obj in onnx_model.graph.output:
        print("name=%r dtype=%r shape=%r" % (
            obj.name, obj.type.tensor_type.elem_type,
            shape2tuple(obj.type.tensor_type.shape)))

    # the list of nodes
    print('** nodes **')
    print(onnx_model.graph.node)

    # in a more nicely format
    print('** nodes **')
    for node in onnx_model.graph.node:
        print("name=%r type=%r input=%r output=%r" % (
            node.name, node.op_type, node.input, node.output))
```

The tensor type is an integer (= 1). The helper function {func}`onnx.helper.tensor_dtype_to_np_dtype` gives the
corresponding type with numpy.

```{eval-rst}
.. exec_code::

    from onnx import TensorProto
    from onnx.helper import tensor_dtype_to_np_dtype, tensor_dtype_to_string

    np_dtype = tensor_dtype_to_np_dtype(TensorProto.FLOAT)
    print(f"The converted numpy dtype for {tensor_dtype_to_string(TensorProto.FLOAT)} is {np_dtype}.")
```

## Serialization

ONNX is built on the top of protobuf. It adds the necessary definitions
to describe a machine learning model and most of the time, ONNX is used
to serialize or deserialize a model. First section addresses this need.
Second section introduces the serialization and deserialization of
data such as tensors, sparse tensors...

### Model Serialization

The model needs to be saved to be deployed.
ONNX is based on protobuf. It minimizes the space needed
to save the graph on disk. Every object (see {ref}`l-onnx-classes`)
in onnx can be serialized with method `SerializeToString`. That's
the case for the whole model.

```{eval-rst}
.. exec_code::

    from onnx import TensorProto
    from onnx.helper import (
        make_model, make_node, make_graph,
        make_tensor_value_info)
    from onnx.checker import check_model

    def shape2tuple(shape):
        return tuple(getattr(d, 'dim_value', 0) for d in shape.dim)

    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])
    node1 = make_node('MatMul', ['X', 'A'], ['XA'])
    node2 = make_node('Add', ['XA', 'B'], ['Y'])
    graph = make_graph([node1, node2], 'lr', [X, A, B], [Y])
    onnx_model = make_model(graph)
    check_model(onnx_model)

    # The serialization
    with open("linear_regression.onnx", "wb") as f:
        f.write(onnx_model.SerializeToString())

    # display
    print(onnx_model)
```

The graph can be restored with function `load`:

```{eval-rst}
.. exec_code::

    from onnx import load

    with open("linear_regression.onnx", "rb") as f:
        onnx_model = load(f)

    # display
    print(onnx_model)
```

It looks exactly the same. Any model can be serialized this way
unless they are bigger than 2 Gb. protobuf is limited to size
smaller than this threshold. Next sections will show how to
overcome that limit.

### Data Serialization

The serialization of tensor usually happens like the following:

```{eval-rst}
.. exec_code::

    import numpy
    from onnx.numpy_helper import from_array

    numpy_tensor = numpy.array([0, 1, 4, 5, 3], dtype=numpy.float32)
    print(type(numpy_tensor))

    onnx_tensor = from_array(numpy_tensor)
    print(type(onnx_tensor))

    serialized_tensor = onnx_tensor.SerializeToString()
    print(type(serialized_tensor))

    with open("saved_tensor.pb", "wb") as f:
        f.write(serialized_tensor)
```

And the deserialization like:

```{eval-rst}
.. exec_code::

    from onnx import TensorProto
    from onnx.numpy_helper import to_array

    with open("saved_tensor.pb", "rb") as f:
        serialized_tensor = f.read()
    print(type(serialized_tensor))

    onnx_tensor = TensorProto()
    onnx_tensor.ParseFromString(serialized_tensor)
    print(type(onnx_tensor))

    numpy_tensor = to_array(onnx_tensor)
    print(numpy_tensor)
```

The same schema can be used for but not limited to {ref}`l-tensorproto`:

```{eval-rst}
.. exec_code::

    import onnx
    import pprint
    pprint.pprint([p for p in dir(onnx)
                   if p.endswith('Proto') and p[0] != '_'])
```

This code can be simplified with function *load_tensor_from_string*
(see {ref}`l-onnx-load-data`).

```{eval-rst}
.. exec_code::

    from onnx import load_tensor_from_string

    with open("saved_tensor.pb", "rb") as f:
        serialized = f.read()
    proto = load_tensor_from_string(serialized)
    print(type(proto))
```

(l-onnx-linear-regression-onnx-api-init)=

## Initializer, default value

The previous model assumed the coefficients of the linear regression
were also input of the model. That's not very convenient. They should be
part of the model itself as constant or **initializer** to follow
onnx semantic. Next example modifies the previous one to change inputs
`A` and `B` into initializers. The package implements two functions to
convert from numpy into onnx and the other way around
(see {ref}`l-numpy-helper-onnx-array`).

- `onnx.numpy_helper.to_array`: converts from onnx to numpy
- `onnx.numpy_helper.from_array`: converts from numpy to onnx

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto
    from onnx.helper import (
        make_model, make_node, make_graph,
        make_tensor_value_info)
    from onnx.checker import check_model

    # initializers
    value = numpy.array([0.5, -0.6], dtype=numpy.float32)
    A = numpy_helper.from_array(value, name='A')

    value = numpy.array([0.4], dtype=numpy.float32)
    C = numpy_helper.from_array(value, name='C')

    # the part which does not change
    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])
    node1 = make_node('MatMul', ['X', 'A'], ['AX'])
    node2 = make_node('Add', ['AX', 'C'], ['Y'])
    graph = make_graph([node1, node2], 'lr', [X], [Y], [A, C])
    onnx_model = make_model(graph)
    check_model(onnx_model)

    print(onnx_model)
```

```{image} images/dot_linreg2.png
```

Again, it is possible to go through the onnx structure to check
how the initializers look like.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto
    from onnx.helper import (
        make_model, make_node, make_graph,
        make_tensor_value_info)
    from onnx.checker import check_model

    # initializers
    value = numpy.array([0.5, -0.6], dtype=numpy.float32)
    A = numpy_helper.from_array(value, name='A')

    value = numpy.array([0.4], dtype=numpy.float32)
    C = numpy_helper.from_array(value, name='C')

    # the part which does not change
    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])
    node1 = make_node('MatMul', ['X', 'A'], ['AX'])
    node2 = make_node('Add', ['AX', 'C'], ['Y'])
    graph = make_graph([node1, node2], 'lr', [X], [Y], [A, C])
    onnx_model = make_model(graph)
    check_model(onnx_model)

    print('** initializer **')
    for init in onnx_model.graph.initializer:
        print(init)
```

The type is defined as integer as well with the same meaning.
In this second example, there is only one input left.
Input `A` and `B` were removed. They could be kept. In that case,
they are optional: every initiliazer sharing the same name as input
is considered as a default value. It replaces the input if this one
is not given.

## Attributes

Some operators need attributes such as {ref}`l-onnx-doc-Transpose` operator.
Let's build the graph for expression $y = XA' + B$ or
`y = Add(MatMul(X, Transpose(A)) + B)`. Transpose needs an attribute
defining the permutation of axes: `perm=[1, 0]`. It is added
as a named attribute in function `make_node`.

```{eval-rst}
.. exec_code::

    from onnx import TensorProto
    from onnx.helper import (
        make_model, make_node, make_graph,
        make_tensor_value_info)
    from onnx.checker import check_model

    # unchanged
    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])

    # added
    node_transpose = make_node('Transpose', ['A'], ['tA'], perm=[1, 0])

    # unchanged except A is replaced by tA
    node1 = make_node('MatMul', ['X', 'tA'], ['XA'])
    node2 = make_node('Add', ['XA', 'B'], ['Y'])

    # node_transpose is added to the list
    graph = make_graph([node_transpose, node1, node2],
                       'lr', [X, A, B], [Y])
    onnx_model = make_model(graph)
    check_model(onnx_model)

    # the work is done, let's display it...
    print(onnx_model)
```

```{image} images/dot_att.png
```

The whole list of *make* functions is the following. Many of them
are described in section {ref}`l-onnx-make-function`.

```{eval-rst}
.. exec_code::

    import onnx
    import pprint
    pprint.pprint([k for k in dir(onnx.helper)
                   if k.startswith('make')])
```

## Opset and metadata

Let's load the ONNX file previously created and check
what kind of metadata it has.

```{eval-rst}
.. exec_code::

    from onnx import load

    with open("linear_regression.onnx", "rb") as f:
        onnx_model = load(f)

    for field in ['doc_string', 'domain', 'functions',
                  'ir_version', 'metadata_props', 'model_version',
                  'opset_import', 'producer_name', 'producer_version',
                  'training_info']:
        print(field, getattr(onnx_model, field))
```

Most of them are empty because it was not filled when the ONNX
graph was created. Two of them have a value:

```{eval-rst}
.. exec_code::

    from onnx import load

    with open("linear_regression.onnx", "rb") as f:
        onnx_model = load(f)

    print("ir_version:", onnx_model.ir_version)
    for opset in onnx_model.opset_import:
        print("opset domain=%r version=%r" % (opset.domain, opset.version))
```

`IR` defined the version of ONNX language.
Opset defines the version of operators being used.
Without any precision, ONNX uses the latest version available
coming from the installed package.
Another one can be used.

```{eval-rst}
.. exec_code::

    from onnx import load

    with open("linear_regression.onnx", "rb") as f:
        onnx_model = load(f)

    del onnx_model.opset_import[:]
    opset = onnx_model.opset_import.add()
    opset.domain = ''
    opset.version = 14

    for opset in onnx_model.opset_import:
        print("opset domain=%r version=%r" % (opset.domain, opset.version))
```

Any opset can be used as long as all operators are defined
the way ONNX specifies it. Version 5 of operator *Reshape*
defines the shape as an input and not as an attribute like in
version 1. The opset tells which specifications is followed
while describing the graph.

The other metadata can be used to store any information,
to store information about the way the model was generated,
a way to distinguish a model from another one with a version
number.

```{eval-rst}
.. exec_code::

    from onnx import load, helper

    with open("linear_regression.onnx", "rb") as f:
        onnx_model = load(f)

    onnx_model.model_version = 15
    onnx_model.producer_name = "something"
    onnx_model.producer_version = "some other thing"
    onnx_model.doc_string = "documentation about this model"
    prop = onnx_model.metadata_props

    data = dict(key1="value1", key2="value2")
    helper.set_model_props(onnx_model, data)

    print(onnx_model)
```

Field `training_info` can be used to store additional graphs.
See [training_tool_test.py](https://github.com/onnx/onnx/blob/main/onnx/test/training_tool_test.py)
to see how it works.

## Subgraph: test and loops

They are usually grouped in a category called *control flow*.
It is usually better to avoid them as they are not as efficient
as the matrix operation are much faster and optimized.

### If

A test can be implemented with operator {ref}`l-onnx-doc-If`.
It executes one subgraph or another depending on one
boolean. This is not used very often as a function usually
needs the result of many comparisons in a batch.
The following example computes the sum of all floats
in a matrix based on the sign, returns 1 or -1.

```{eval-rst}
.. exec_code::

    import numpy
    import onnx
    from onnx.helper import (
        make_node, make_graph, make_model, make_tensor_value_info)
    from onnx.numpy_helper import from_array
    from onnx.checker import check_model
    from onnxruntime import InferenceSession

    # initializers
    value = numpy.array([0], dtype=numpy.float32)
    zero = from_array(value, name='zero')

    # Same as before, X is the input, Y is the output.
    X = make_tensor_value_info('X', onnx.TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', onnx.TensorProto.FLOAT, [None])

    # The node building the condition. The first one
    # sum over all axes.
    rsum = make_node('ReduceSum', ['X'], ['rsum'])
    # The second compares the result to 0.
    cond = make_node('Greater', ['rsum', 'zero'], ['cond'])

    # Builds the graph is the condition is True.
    # Input for then
    then_out = make_tensor_value_info(
        'then_out', onnx.TensorProto.FLOAT, None)
    # The constant to return.
    then_cst = from_array(numpy.array([1]).astype(numpy.float32))

    # The only node.
    then_const_node = make_node(
        'Constant', inputs=[],
        outputs=['then_out'],
        value=then_cst, name='cst1')

    # And the graph wrapping these elements.
    then_body = make_graph(
        [then_const_node], 'then_body', [], [then_out])

    # Same process for the else branch.
    else_out = make_tensor_value_info(
        'else_out', onnx.TensorProto.FLOAT, [5])
    else_cst = from_array(numpy.array([-1]).astype(numpy.float32))

    else_const_node = make_node(
        'Constant', inputs=[],
        outputs=['else_out'],
        value=else_cst, name='cst2')

    else_body = make_graph(
        [else_const_node], 'else_body',
        [], [else_out])

    # Finally the node If taking both graphs as attributes.
    if_node = onnx.helper.make_node(
        'If', ['cond'], ['Y'],
        then_branch=then_body,
        else_branch=else_body)

    # The final graph.
    graph = make_graph([rsum, cond, if_node], 'if', [X], [Y], [zero])
    onnx_model = make_model(graph)
    check_model(onnx_model)

    # Let's freeze the opset.
    del onnx_model.opset_import[:]
    opset = onnx_model.opset_import.add()
    opset.domain = ''
    opset.version = 15
    onnx_model.ir_version = 8

    # Save.
    with open("onnx_if_sign.onnx", "wb") as f:
        f.write(onnx_model.SerializeToString())

    # Let's see the output.
    sess = InferenceSession(onnx_model.SerializeToString(),
                            providers=["CPUExecutionProvider"])

    x = numpy.ones((3, 2), dtype=numpy.float32)
    res = sess.run(None, {'X': x})

    # It works.
    print("result", res)
    print()

    # Some display.
    print(onnx_model)
```

The whole is easier to visualize with the following image.

```{image} images/dot_if_py.png
```

Both else and then branches are very simple.
Node *If* could even be replaced with a node *Where* and
that would be faster. It becomes interesting when both branches
are bigger and skipping one is more efficient.

### Scan

{ref}`l-onnx-doc-Scan` seems quite complex when reading the specifications.
It is useful to loop over one dimension of a tensor and store
the results in a preallocated tensor.

The following example implements a classic nearest neighbors for
a regression problem. The first step consists in computing the
pairwise distances between the input features *X* and the training
set *W*: $dist(X,W) = (M_{ij}) = (\norm{X_i - W_j}^2)_{ij}$. It is
followed by an operator {ref}`l-onnx-doc-TopK` which extracts the *k* nearest
neighbors.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto
    from onnx.helper import (
        make_model, make_node, set_model_props, make_tensor, make_graph,
        make_tensor_value_info)
    from onnx.checker import check_model

    # subgraph
    initializers = []
    nodes = []
    inputs = []
    outputs = []

    value = make_tensor_value_info('next_in', 1, [None, 4])
    inputs.append(value)
    value = make_tensor_value_info('next', 1, [None])
    inputs.append(value)

    value = make_tensor_value_info('next_out', 1, [None, None])
    outputs.append(value)
    value = make_tensor_value_info('scan_out', 1, [None])
    outputs.append(value)

    node = make_node(
        'Identity', ['next_in'], ['next_out'],
        name='cdistd_17_Identity', domain='')
    nodes.append(node)

    node = make_node(
        'Sub', ['next_in', 'next'], ['cdistdf_17_C0'],
        name='cdistdf_17_Sub', domain='')
    nodes.append(node)

    node = make_node(
        'ReduceSumSquare', ['cdistdf_17_C0'], ['cdistdf_17_reduced0'],
        name='cdistdf_17_ReduceSumSquare', axes=[1], keepdims=0, domain='')
    nodes.append(node)

    node = make_node(
        'Identity', ['cdistdf_17_reduced0'],
        ['scan_out'], name='cdistdf_17_Identity', domain='')
    nodes.append(node)

    graph = make_graph(nodes, 'OnnxIdentity',
                       inputs, outputs, initializers)

    # main graph

    initializers = []
    nodes = []
    inputs = []
    outputs = []

    opsets = {'': 15, 'ai.onnx.ml': 15}
    target_opset = 15  # subgraphs

    # initializers
    list_value = [23.29599822460675, -120.86516699239603, -144.70495899914215, -260.08772982740413,
                  154.65272105889147, -122.23295157108991, 247.45232560871727, -182.83789715805776,
                  -132.92727431421793, 147.48710175784703, 88.27761768038069, -14.87785569894749,
                  111.71487894705504, 301.0518319089629, -29.64235742280055, -113.78493504731911,
                  -204.41218591022718, 112.26561056133608, 66.04032954135549,
                  -229.5428380626701, -33.549262642481615, -140.95737409864623, -87.8145187836131,
                  -90.61397011283958, 57.185488100413366, 56.864151796743855, 77.09054590340892,
                  -187.72501631246712, -42.779503579806025, -21.642642730674076, -44.58517761667535,
                  78.56025104939847, -23.92423223842056, 234.9166231927213, -73.73512816431007,
                  -10.150864499514297, -70.37105466673813, 65.5755688281476, 108.68676290979731, -78.36748960443065]
    value = numpy.array(list_value, dtype=numpy.float64).reshape((2, 20))
    tensor = numpy_helper.from_array(
        value, name='knny_ArrayFeatureExtractorcst')
    initializers.append(tensor)

    list_value = [1.1394007205963135, -0.6848101019859314, -1.234825849533081, 0.4023416340351105,
                  0.17742614448070526, 0.46278226375579834, -0.4017809331417084, -1.630198359489441,
                  -0.5096521973609924, 0.7774903774261475, -0.4380742907524109, -1.2527953386306763,
                  -1.0485529899597168, 1.950775384902954, -1.420017957687378, -1.7062702178955078,
                  1.8675580024719238, -0.15135720372200012, -0.9772778749465942, 0.9500884413719177,
                  -2.5529897212982178, -0.7421650290489197, 0.653618574142456, 0.8644362092018127,
                  1.5327792167663574, 0.37816253304481506, 1.4693588018417358, 0.154947429895401,
                  -0.6724604368209839, -1.7262825965881348, -0.35955315828323364, -0.8131462931632996,
                  -0.8707971572875977, 0.056165341287851334, -0.5788496732711792, -0.3115525245666504,
                  1.2302906513214111, -0.302302747964859, 1.202379822731018, -0.38732680678367615,
                  2.269754648208618, -0.18718385696411133, -1.4543657302856445, 0.04575851559638977,
                  -0.9072983860969543, 0.12898291647434235, 0.05194539576768875, 0.7290905714035034,
                  1.4940791130065918, -0.8540957570075989, -0.2051582634449005, 0.3130677044391632,
                  1.764052391052246, 2.2408931255340576, 0.40015721321105957, 0.978738009929657,
                  0.06651721894741058, -0.3627411723136902, 0.30247190594673157, -0.6343221068382263,
                  -0.5108051300048828, 0.4283318817615509, -1.18063223361969, -0.02818222902715206,
                  -1.6138978004455566, 0.38690251111984253, -0.21274028718471527, -0.8954665660858154,
                  0.7610377073287964, 0.3336743414402008, 0.12167501449584961, 0.44386324286460876,
                  -0.10321885347366333, 1.4542734622955322, 0.4105985164642334, 0.14404356479644775,
                  -0.8877857327461243, 0.15634897351264954, -1.980796456336975, -0.34791216254234314]
    value = numpy.array(list_value, dtype=numpy.float32).reshape((20, 4))
    tensor = numpy_helper.from_array(value, name='Sc_Scancst')
    initializers.append(tensor)

    value = numpy.array([2], dtype=numpy.int64)
    tensor = numpy_helper.from_array(value, name='To_TopKcst')
    initializers.append(tensor)

    value = numpy.array([2, -1, 2], dtype=numpy.int64)
    tensor = numpy_helper.from_array(value, name='knny_Reshapecst')
    initializers.append(tensor)

    # inputs
    value = make_tensor_value_info('input', 1, [None, 4])
    inputs.append(value)

    # outputs
    value = make_tensor_value_info('variable', 1, [None, 2])
    outputs.append(value)

    # nodes

    node = make_node(
        'Scan', ['input', 'Sc_Scancst'], ['UU032UU', 'UU033UU'],
        name='Sc_Scan', body=graph, num_scan_inputs=1, domain='')
    nodes.append(node)

    node = make_node(
        'Transpose', ['UU033UU'], ['Tr_transposed0'],
        name='Tr_Transpose', perm=[1, 0], domain='')
    nodes.append(node)

    node = make_node(
        'Sqrt', ['Tr_transposed0'], ['Sq_Y0'],
        name='Sq_Sqrt', domain='')
    nodes.append(node)

    node = make_node(
        'TopK', ['Sq_Y0', 'To_TopKcst'], ['To_Values0', 'To_Indices1'],
        name='To_TopK', largest=0, sorted=1, domain='')
    nodes.append(node)

    node = make_node(
        'Flatten', ['To_Indices1'], ['knny_output0'],
        name='knny_Flatten', domain='')
    nodes.append(node)

    node = make_node(
        'ArrayFeatureExtractor',
        ['knny_ArrayFeatureExtractorcst', 'knny_output0'], ['knny_Z0'],
        name='knny_ArrayFeatureExtractor', domain='ai.onnx.ml')
    nodes.append(node)

    node = make_node(
        'Reshape', ['knny_Z0', 'knny_Reshapecst'], ['knny_reshaped0'],
        name='knny_Reshape', allowzero=0, domain='')
    nodes.append(node)

    node = make_node(
        'Transpose', ['knny_reshaped0'], ['knny_transposed0'],
        name='knny_Transpose', perm=[1, 0, 2], domain='')
    nodes.append(node)

    node = make_node(
        'Cast', ['knny_transposed0'], ['Ca_output0'],
        name='Ca_Cast', to=TensorProto.FLOAT, domain='')
    nodes.append(node)

    node = make_node(
        'ReduceMean', ['Ca_output0'], ['variable'],
        name='Re_ReduceMean', axes=[2], keepdims=0, domain='')
    nodes.append(node)

    # graph
    graph = make_graph(nodes, 'KNN regressor', inputs, outputs, initializers)

    # model
    onnx_model = make_model(graph)
    onnx_model.ir_version = 8
    onnx_model.producer_name = 'skl2onnx'
    onnx_model.producer_version = ''
    onnx_model.domain = 'ai.onnx'
    onnx_model.model_version = 0
    onnx_model.doc_string = ''
    set_model_props(onnx_model, {})

    # opsets
    del onnx_model.opset_import[:]
    for dom, value in opsets.items():
        op_set = onnx_model.opset_import.add()
        op_set.domain = dom
        op_set.version = value

    check_model(onnx_model)
    with open("knnr.onnx", "wb") as f:
        f.write(onnx_model.SerializeToString())

    print(onnx_model)
```

Visually it looks like the following:

```{image} images/dot_scan_py.png
```

The subgraph is executed by operator {ref}`l-onnx-doc-Scan`. In this case,
there is one *scan* input meaning the operator only builds one output.

```
node = make_node(
    'Scan', ['X1', 'X2'], ['Y1', 'Y2'],
    name='Sc_Scan', body=graph, num_scan_inputs=1, domain='')
```

At the first iteration, the subgraph gets *X1* and the first row of *X2*.
The graph produces two outputs. The first one replaces *X1* in the next iteration,
the second one is store in a container to form *Y2*. At the second iteration,
second input of the subgraph is the second row of *X2*.
Here is a short summary. Green is the first iteration, blue the second.

```{image} images/scanop.png
:width: 400
```

## Functions

As mentioned in previous chapter, functions can be used to shorten
the code to build the model and offer more possibilities to the runtime
running predictions to be faster if there exists a specific implementation
of this function. If it is not the case, the runtime can still use
the default implementation based on existing operators.

Function `make_function` is used to define a function.
It works like a graph with less types. It is more like a
template. This API may evolve. It does not include initializers either.

### A function with no attribute

That's the more simple case. Every input of the function is a dynamic
object known at execution time.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto
    from onnx.helper import (
        make_model, make_node, set_model_props, make_tensor,
        make_graph, make_tensor_value_info, make_opsetid,
        make_function)
    from onnx.checker import check_model

    new_domain = 'custom'
    opset_imports = [make_opsetid("", 14), make_opsetid(new_domain, 1)]

    # Let's define a function for a linear regression

    node1 = make_node('MatMul', ['X', 'A'], ['XA'])
    node2 = make_node('Add', ['XA', 'B'], ['Y'])

    linear_regression = make_function(
        new_domain,            # domain name
        'LinearRegression',     # function name
        ['X', 'A', 'B'],        # input names
        ['Y'],                  # output names
        [node1, node2],         # nodes
        opset_imports,          # opsets
        [])                     # attribute names

    # Let's use it in a graph.

    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])

    graph = make_graph(
        [make_node('LinearRegression', ['X', 'A', 'B'], ['Y1'], domain=new_domain),
         make_node('Abs', ['Y1'], ['Y'])],
        'example',
        [X, A, B], [Y])

    onnx_model = make_model(
        graph, opset_imports=opset_imports,
        functions=[linear_regression])  # functions to add)
    check_model(onnx_model)

    # the work is done, let's display it...
    print(onnx_model)
```

### A function with attributes

```{index} ref_attr_name
```

The following functions are equivalent to the previous one except
one input, *B*, was converted into an argument named *bias*.
The code is almost the same except the bias is now a constant.
Inside the function definition, a node *Constant* is created
to insert the argument as a result. It is linked to the argument
with the attribute `ref_attr_name`.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto, AttributeProto
    from onnx.helper import (
        make_model, make_node, set_model_props, make_tensor,
        make_graph, make_tensor_value_info, make_opsetid,
        make_function)
    from onnx.checker import check_model

    new_domain = 'custom'
    opset_imports = [make_opsetid("", 14), make_opsetid(new_domain, 1)]

    # Let's define a function for a linear regression
    # The first step consists in creating a constant
    # equal to the input parameter of the function.
    cst = make_node('Constant',  [], ['B'])

    att = AttributeProto()
    att.name = "value"

    # This line indicates the value comes from the argument
    # named 'bias' the function is given.
    att.ref_attr_name = "bias"
    att.type = AttributeProto.TENSOR
    cst.attribute.append(att)

    node1 = make_node('MatMul', ['X', 'A'], ['XA'])
    node2 = make_node('Add', ['XA', 'B'], ['Y'])

    linear_regression = make_function(
        new_domain,            # domain name
        'LinearRegression',     # function name
        ['X', 'A'],             # input names
        ['Y'],                  # output names
        [cst, node1, node2],    # nodes
        opset_imports,          # opsets
        ["bias"])               # attribute names

    # Let's use it in a graph.

    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])

    graph = make_graph(
        [make_node('LinearRegression', ['X', 'A'], ['Y1'], domain=new_domain,
                   # bias is now an argument of the function and is defined as a tensor
                   bias=make_tensor('former_B', TensorProto.FLOAT, [1], [0.67])),
         make_node('Abs', ['Y1'], ['Y'])],
        'example',
        [X, A], [Y])

    onnx_model = make_model(
        graph, opset_imports=opset_imports,
        functions=[linear_regression])  # functions to add)
    check_model(onnx_model)

    # the work is done, let's display it...
    print(onnx_model)
```

## Parsing

Module onnx provides a faster way to define a graph
and is lot easier to read. That's easy to use when the graph is built
in a single function, less easy when the graph is built from many
different functions converting each piece of a machine learning
pipeline.

```
import onnx.parser
from onnx.checker import check_model

input = '''
    <
        ir_version: 8,
        opset_import: [ "" : 15]
    >
    agraph (float[I,J] X, float[I] A, float[I] B) => (float[I] Y) {
        XA = MatMul(X, A)
        Y = Add(XA, B)
    }
    '''
onnx_model = onnx.parser.parse_model(input)
check_model(onnx_model)

print(onnx_model)
```

```
ir_version: 8
graph {
node {
    input: "X"
    input: "A"
    output: "XA"
    op_type: "MatMul"
    domain: ""
}
node {
    input: "XA"
    input: "B"
    output: "Y"
    op_type: "Add"
    domain: ""
}
name: "agraph"
input {
    name: "X"
    type {
    tensor_type {
        elem_type: 1
        shape {
        dim {
            dim_param: "I"
        }
        dim {
            dim_param: "J"
        }
        }
    }
    }
}
input {
    name: "A"
    type {
    tensor_type {
        elem_type: 1
        shape {
        dim {
            dim_param: "I"
        }
        }
    }
    }
}
input {
    name: "B"
    type {
    tensor_type {
        elem_type: 1
        shape {
        dim {
            dim_param: "I"
        }
        }
    }
    }
}
output {
    name: "Y"
    type {
    tensor_type {
        elem_type: 1
        shape {
        dim {
            dim_param: "I"
        }
        }
    }
    }
}
}
opset_import {
domain: ""
version: 15
}
```

This way is used to create small models but it is rarely used
in converting libraries.

## Checker and Shape Inference

onnx provides a function to check the model is valid.
It checks input type or shapes whenever it can detect inconsistency.
The following example adds two matrices of different types
which is not allowed.

```{eval-rst}
.. exec_code::

    import onnx.parser
    import onnx.checker

    input = '''
        <
            ir_version: 8,
            opset_import: [ "" : 15]
        >
        agraph (float[I,4] X, float[4,2] A, int[4] B) => (float[I] Y) {
            XA = MatMul(X, A)
            Y = Add(XA, B)
        }
        '''
    try:
        onnx_model = onnx.parser.parse_model(input)
        onnx.checker.check_model(onnx_model)
    except Exception as e:
        print(e)
```

`check_model` raises an error due to that inconsistency.
This work for all operators defined in the main domain or the ML domain.
It remains silent for any custom operator not defined in any specification.

Shape inference serves one purpose: estimate the shape
and the type of intermediate results.
If known, the runtime can estimate the memory consumption
beforehand and optimize the computation. It can fuse some
operators, it can do the computation inplace...

```{eval-rst}
.. exec_code::

    import onnx.parser
    from onnx import helper, shape_inference

    input = '''
        <
            ir_version: 8,
            opset_import: [ "" : 15]
        >
        agraph (float[I,4] X, float[4,2] A, float[4] B) => (float[I] Y) {
            XA = MatMul(X, A)
            Y = Add(XA, B)
        }
        '''
    onnx_model = onnx.parser.parse_model(input)
    inferred_model = shape_inference.infer_shapes(onnx_model)

    print(inferred_model)
```

There is a new attribute `value_info` which stores the inferred shapes.
Letter `I` in `dim_param: "I"` can be seen as a variable. It depends on the inputs
but the function is able to tell which intermediate result will share
the same dimension.
Shape inference does not work all the time. For example,
a Reshape operator. Shape inference only works if the shape is constant.
If not constant, the shape cannot be easily inferred unless
the following nodes expect specific shape.

## Evaluation and Runtime

The ONNX standard allows frameworks to export trained models in ONNX format,
and enables inference using any backend that supports the ONNX format.
*onnxruntime* is one efficient option. It is available in many platforms.
It is optimized for fast inference. Its coverage can be tracked on
[ONNX Backend Dashboard](https://onnx.ai/backend-scoreboard/).
*onnx* implements a python runtime useful to help understand a model.
It is not intended to be used for production and performance is not a goal.

### Evaluation of a linear regression

Full API is described at {ref}`l-reference-implementation`.
It takes a model (a *ModelProto*, a filename, ...).
Method `run` returns the outputs for a given set of inputs
specified in a dictionary.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto
    from onnx.helper import (
        make_model, make_node, set_model_props, make_tensor,
        make_graph, make_tensor_value_info)
    from onnx.checker import check_model
    from onnx.reference import ReferenceEvaluator

    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])
    node1 = make_node('MatMul', ['X', 'A'], ['XA'])
    node2 = make_node('Add', ['XA', 'B'], ['Y'])
    graph = make_graph([node1, node2], 'lr', [X, A, B], [Y])
    onnx_model = make_model(graph)
    check_model(onnx_model)

    sess = ReferenceEvaluator(onnx_model)

    x = numpy.random.randn(4, 2).astype(numpy.float32)
    a = numpy.random.randn(2, 1).astype(numpy.float32)
    b = numpy.random.randn(1, 1).astype(numpy.float32)
    feeds = {'X': x, 'A': a, 'B': b}

    print(sess.run(None, feeds))
```

### Evaluation of a node

The evaluator can also evaluate a simple node to check how an operator
behaves on a specific input.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto
    from onnx.helper import make_node

    from onnx.reference import ReferenceEvaluator

    node = make_node('EyeLike', ['X'], ['Y'])

    sess = ReferenceEvaluator(node)

    x = numpy.random.randn(4, 2).astype(numpy.float32)
    feeds = {'X': x}

    print(sess.run(None, feeds))
```

Similar code would also work on *GraphProto* or *FunctionProto*.

### Evaluation Step by Step

A converting library takes an existing model trained with a machine
learning framework (*pytorch*, *scikit-learn*, ...) and
converts the model into an ONNX graph. Complex models usually do not work
on the first try and seeing intermediate results may help to find the
part incorrectly converted. Parameter `verbose` displays information
about intermediate results.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto
    from onnx.helper import (
        make_model, make_node, set_model_props, make_tensor,
        make_graph, make_tensor_value_info)
    from onnx.checker import check_model
    from onnx.reference import ReferenceEvaluator

    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])
    node1 = make_node('MatMul', ['X', 'A'], ['XA'])
    node2 = make_node('Add', ['XA', 'B'], ['Y'])
    graph = make_graph([node1, node2], 'lr', [X, A, B], [Y])
    onnx_model = make_model(graph)
    check_model(onnx_model)

    for verbose in [1, 2, 3, 4]:
        print()
        print(f"------ verbose={verbose}")
        print()
        sess = ReferenceEvaluator(onnx_model, verbose=verbose)

        x = numpy.random.randn(4, 2).astype(numpy.float32)
        a = numpy.random.randn(2, 1).astype(numpy.float32)
        b = numpy.random.randn(1, 1).astype(numpy.float32)
        feeds = {'X': x, 'A': a, 'B': b}

        print(sess.run(None, feeds))
```

### Evaluate a custom node

The following example still implements a linear regression
but adds the identity matrix to *A*: $Y = X(A + I) + B$.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto
    from onnx.helper import (
        make_model, make_node, set_model_props, make_tensor,
        make_graph, make_tensor_value_info)
    from onnx.checker import check_model
    from onnx.reference import ReferenceEvaluator

    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])
    node0 = make_node('EyeLike', ['A'], ['Eye'])
    node1 = make_node('Add', ['A', 'Eye'], ['A1'])
    node2 = make_node('MatMul', ['X', 'A1'], ['XA1'])
    node3 = make_node('Add', ['XA1', 'B'], ['Y'])
    graph = make_graph([node0, node1, node2, node3], 'lr', [X, A, B], [Y])
    onnx_model = make_model(graph)
    check_model(onnx_model)
    with open("linear_regression.onnx", "wb") as f:
        f.write(onnx_model.SerializeToString())

    sess = ReferenceEvaluator(onnx_model, verbose=2)

    x = numpy.random.randn(4, 2).astype(numpy.float32)
    a = numpy.random.randn(2, 2).astype(numpy.float32) / 10
    b = numpy.random.randn(1, 2).astype(numpy.float32)
    feeds = {'X': x, 'A': a, 'B': b}

    print(sess.run(None, feeds))
```

What if we combine operators *EyeLike* and *Add* into *AddEyeLike* to
make it more efficient. Next example replaces these two operators
by a single one from domain `'optimized'`.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto
    from onnx.helper import (
        make_model, make_node, set_model_props, make_tensor,
        make_graph, make_tensor_value_info, make_opsetid)
    from onnx.checker import check_model

    X = make_tensor_value_info('X', TensorProto.FLOAT, [None, None])
    A = make_tensor_value_info('A', TensorProto.FLOAT, [None, None])
    B = make_tensor_value_info('B', TensorProto.FLOAT, [None, None])
    Y = make_tensor_value_info('Y', TensorProto.FLOAT, [None])

    node01 = make_node('AddEyeLike', ['A'], ['A1'], domain='optimized')

    node2 = make_node('MatMul', ['X', 'A1'], ['XA1'])
    node3 = make_node('Add', ['XA1', 'B'], ['Y'])
    graph = make_graph([node01, node2, node3], 'lr', [X, A, B], [Y])

    onnx_model = make_model(graph, opset_imports=[
        make_opsetid('', 18), make_opsetid('optimized', 1)
    ])

    check_model(onnx_model)
    with open("linear_regression_improved.onnx", "wb") as f:
        f.write(onnx_model.SerializeToString())
```

We need to evaluate this model is equivalent to the first one.
This requires an implementation for this particular node.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx.reference import ReferenceEvaluator
    from onnx.reference.op_run import OpRun

    class AddEyeLike(OpRun):

        op_domain = "optimized"

        def _run(self, X, alpha=1.):
            assert len(X.shape) == 2
            assert X.shape[0] == X.shape[1]
            X = X.copy()
            ind = numpy.diag_indices(X.shape[0])
            X[ind] += alpha
            return (X,)

    sess = ReferenceEvaluator("linear_regression_improved.onnx", verbose=2, new_ops=[AddEyeLike])

    x = numpy.random.randn(4, 2).astype(numpy.float32)
    a = numpy.random.randn(2, 2).astype(numpy.float32) / 10
    b = numpy.random.randn(1, 2).astype(numpy.float32)
    feeds = {'X': x, 'A': a, 'B': b}

    print(sess.run(None, feeds))

    # Let's check with the previous model.

    sess0 = ReferenceEvaluator("linear_regression.onnx",)
    sess1 = ReferenceEvaluator("linear_regression_improved.onnx", new_ops=[AddEyeLike])

    y0 = sess0.run(None, feeds)[0]
    y1 = sess1.run(None, feeds)[0]
    print(y0)
    print(y1)
    print(f"difference: {numpy.abs(y0 - y1).max()}")
```

Predictions are the same. Let's compare the performance
on a matrix big enough to see a significant difference.

```{eval-rst}
.. exec_code::

    import timeit
    import numpy
    from onnx.reference import ReferenceEvaluator
    from onnx.reference.op_run import OpRun

    class AddEyeLike(OpRun):

        op_domain = "optimized"

        def _run(self, X, alpha=1.):
            assert len(X.shape) == 2
            assert X.shape[0] == X.shape[1]
            X = X.copy()
            ind = numpy.diag_indices(X.shape[0])
            X[ind] += alpha
            return (X,)

    sess = ReferenceEvaluator("linear_regression_improved.onnx", verbose=2, new_ops=[AddEyeLike])

    x = numpy.random.randn(4, 100).astype(numpy.float32)
    a = numpy.random.randn(100, 100).astype(numpy.float32) / 10
    b = numpy.random.randn(1, 100).astype(numpy.float32)
    feeds = {'X': x, 'A': a, 'B': b}

    sess0 = ReferenceEvaluator("linear_regression.onnx")
    sess1 = ReferenceEvaluator("linear_regression_improved.onnx", new_ops=[AddEyeLike])

    y0 = sess0.run(None, feeds)[0]
    y1 = sess1.run(None, feeds)[0]
    print(f"difference: {numpy.abs(y0 - y1).max()}")
    print(f"time with EyeLike+Add: {timeit.timeit(lambda: sess0.run(None, feeds), number=1000)}")
    print(f"time with AddEyeLike: {timeit.timeit(lambda: sess1.run(None, feeds), number=1000)}")
```

It seems worth adding an optimized node in this case.
This kind of optimization is usually called *fusion*.
Two consecutive operators are fused into an optimized version of both.
Production usually relies on *onnxruntime* but since
the optimization uses basic matrix operation, it should bring
the same performance gain on any other runtime.

## Implementation details

### Python and C++

onnx relies on protobuf to define its type.
You would assume that a python object is just a wrapper around
a C pointer on the internal structure. Therefore, it should be
possible to access internal data from a function receiving a python
object of type `ModelProto`. But it is not. According to
[Protobuf 4, changes](https://developers.google.com/protocol-buffers/docs/news/2022-05-06),
this is no longer possible after version 4 and it is safer to assume the
only way to get a hold on the content is to serialize the model
into bytes, give it to the C function, then deserialize it.
Functions like `check_model` or
`shape_inference` are calling `SerializeToString` then
`ParseFromString` before checking the model with a C code.

### Attributes and inputs

There is a clear distinction between the two. Inputs are dynamic and
may change at every execution. Attributes never changes and an optimizer
can improve the execution graph assuming it never changes.
Therefore, it is impossible to turn an input into an attribute.
And the operator *Constant* is the only operator changing an
attribute into an input.

### Shape or no shape

onnx usually expects a shape for every input or output
assuming the rank (or the number of dimensions) is known.
What if we need to create a valid graph for every dimension?
This case is still puzzling.

```{eval-rst}
.. exec_code::

    import numpy
    from onnx import numpy_helper, TensorProto, FunctionProto
    from onnx.helper import (
        make_model, make_node, set_model_props, make_tensor,
        make_graph, make_tensor_value_info, make_opsetid,
        make_function)
    from onnx.checker import check_model
    from onnxruntime import InferenceSession

    def create_model(shapes):
        new_domain = 'custom'
        opset_imports = [make_opsetid("", 14), make_opsetid(new_domain, 1)]

        node1 = make_node('MatMul', ['X', 'A'], ['XA'])
        node2 = make_node('Add', ['XA', 'A'], ['Y'])

        X = make_tensor_value_info('X', TensorProto.FLOAT, shapes['X'])
        A = make_tensor_value_info('A', TensorProto.FLOAT, shapes['A'])
        Y = make_tensor_value_info('Y', TensorProto.FLOAT, shapes['Y'])

        graph = make_graph([node1, node2], 'example', [X, A], [Y])

        onnx_model = make_model(graph, opset_imports=opset_imports)
        # Let models runnable by onnxruntime with a released ir_version
        onnx_model.ir_version = 8

        return onnx_model

    print("----------- case 1: 2D x 2D -> 2D")
    onnx_model = create_model({'X': [None, None], 'A': [None, None], 'Y': [None, None]})
    check_model(onnx_model)
    sess = InferenceSession(onnx_model.SerializeToString(),
                            providers=["CPUExecutionProvider"])
    res = sess.run(None, {
        'X': numpy.random.randn(2, 2).astype(numpy.float32),
        'A': numpy.random.randn(2, 2).astype(numpy.float32)})
    print(res)

    print("----------- case 2: 2D x 1D -> 1D")
    onnx_model = create_model({'X': [None, None], 'A': [None], 'Y': [None]})
    check_model(onnx_model)
    sess = InferenceSession(onnx_model.SerializeToString(),
                            providers=["CPUExecutionProvider"])
    res = sess.run(None, {
        'X': numpy.random.randn(2, 2).astype(numpy.float32),
        'A': numpy.random.randn(2).astype(numpy.float32)})
    print(res)

    print("----------- case 3: 2D x 0D -> 0D")
    onnx_model = create_model({'X': [None, None], 'A': [], 'Y': []})
    check_model(onnx_model)
    try:
        InferenceSession(onnx_model.SerializeToString(),
                         providers=["CPUExecutionProvider"])
    except Exception as e:
        print(e)

    print("----------- case 4: 2D x None -> None")
    onnx_model = create_model({'X': [None, None], 'A': None, 'Y': None})
    try:
        check_model(onnx_model)
    except Exception as e:
        print(type(e), e)
    sess = InferenceSession(onnx_model.SerializeToString(),
                            providers=["CPUExecutionProvider"])
    res = sess.run(None, {
        'X': numpy.random.randn(2, 2).astype(numpy.float32),
        'A': numpy.random.randn(2).astype(numpy.float32)})
    print(res)
    print("----------- end")
```