module npy.xop

Short summary

module mlprodict.npy.xop

Xop API to build onnx graphs. Inspired from sklearn-onnx.

Classes

class	truncated documentation
_GraphBuilder	Graph builder. It takes a graph structure made with instances of OnnxOperatorBase. The main method is to_onnx. …
OnnxLoadFactory	Automatically creating all operators from onnx packages takes time. That’s why function loadop only creates …
OnnxOperator	Ancestor to every ONNX operator exposed in mlprodict.npy.xops and mlprodict.npy.xops_ml.
OnnxOperatorBase	Base class for OnnxOperator, OnnxOperator`Item, :class:`OnnxOperatorTuple.
OnnxOperatorFunction	This operator is used to insert existing ONNX function into the ONNX graph being built.
OnnxOperatorItem	Accessor to one of the output returned by a OnnxOperator.
OnnxOperatorTuple	Class used to return multiple OnnxVar at the same time.

Functions

function	truncated documentation
_default_OPSET_TO_IR_VERSION	Returns the default mapping between opset and ir_version.
_domain_to_class_name	Converts domain into a name.
_dynamic_class_creation	Automatically generates classes for each of the operators module onnx defines and described at Operators …
_find_operator_domain	Determines the domain of an operator. Raises an exception if not found or if there is an ambiguity.
_populate_schemas	Populates all schemas.
ClassFactory	Dynamically creates a class for a specific operator.
loadop	Dynamically creates a class for a every operator type in the given list.

Properties

property	truncated documentation
inputs	Returns the only inputs in a list.
inputs	Returns the only inputs in a list.
output_names	Returns self.output_names_.
output_names	This method should be overwritten.
output_names	Returns self.output_names_.
output_names	Returns None.
output_names	Returns ‘output_names’ of attribute ‘unique’.
outputs	Returns ‘output_names’ of attribute ‘unique’.

Static Methods

staticmethod	truncated documentation
__class_getitem__	Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be …
__class_getitem__	Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be …
_merge_op_version
_merge_op_version
_node_to_graph_get_type
_node_to_graph_get_type
_node_to_graph_preprocess_list
_node_to_graph_preprocess_list
_node_to_graph_process_input
_node_to_graph_process_input
_node_to_graph_reorder_by_name
_node_to_graph_reorder_by_name
attribute_to_value	Converts an attribute into a value using python structures.
number2alpha	Converts a numbers into a string keeping the same alphabetical order.

Methods

method	truncated documentation
__abs__	Automatically adds operator OnnxAbs to the graph.
__abs__	Automatically adds operator OnnxAbs to the graph.
__add__	Automatically adds operator OnnxAdd to the graph.
__add__	Automatically adds operator OnnxAdd to the graph.
__call__	Creates an instance of class OnnxOperatorFunction. Equivalent to OnnxOperatorFunction(proto, *args, **kwargs). …
__call__	Creates an instance of class OnnxOperatorFunction. Equivalent to OnnxOperatorFunction(proto, *args, **kwargs). …
__eq__	Automatically adds operator OnnxEqual to the graph.
__eq__	Automatically adds operator OnnxEqual to the graph.
__getattr__	Enables expressions such as:
__getitem__	Returns an accessor to one of the output of this node.
__getitem__	Returns an accessor to one of the output of this node.
__getitem__	usual
__gt__	Automatically adds operator OnnxGreater to the graph.
__gt__	Automatically adds operator OnnxGreater to the graph.
__init__
__init__
__init__
__init__
__init__
__init__
__init__
__iter__	Allows expressions such as a, b = OnnxTopK(...).
__iter__	Allows expressions such as a, b = OnnxTopK(...).
__iter__	Iterates on the outputs.
__len__	usual
__lt__	Automatically adds operator OnnxLess to the graph.
__lt__	Automatically adds operator OnnxLess to the graph.
__matmul__	Automatically adds operator OnnxMatMul to the graph.
__matmul__	Automatically adds operator OnnxMatMul to the graph.
__mod__	Automatically adds operator OnnxMod to the graph.
__mod__	Automatically adds operator OnnxMod to the graph.
__mul__	Automatically adds operator OnnxMul to the graph.
__mul__	Automatically adds operator OnnxMul to the graph.
__ne__	Automatically adds operator OnnxNot x OnnxEqual to the graph.
__ne__	Automatically adds operator OnnxNot x OnnxEqual to the graph.
__pow__	Automatically adds operator OnnxPow to the graph.
__pow__	Automatically adds operator OnnxPow to the graph.
__repr__	usual
__repr__	usual
__repr__	usual
__repr__	usual
__str__	usual
__sub__	Automatically adds operator OnnxSub to the graph.
__sub__	Automatically adds operator OnnxSub to the graph.
__truediv__	Automatically adds operator OnnxDiv to the graph.
__truediv__	Automatically adds operator OnnxDiv to the graph.
_add_name
_check
_check
_node_to_graph	Builds a graph as a list of nodes to walk through in that order.
_node_to_graph	Builds a graph as a list of nodes to walk through in that order.
_post_process_attributes	Walks through attributes and replaces them by ONNX values.
_post_process_attributes	Walks through attributes and replaces them by ONNX values.
_process_io
add_function	Adds a function to the graph.
add_initializer	Adds an initializer to the graph.
add_node	Adds a node to the graph.
add_to	Adds to graph builder.
add_to	This method should be overwritten.
add_to	Adds to graph builder.
add_to	Adds to graph builder. Does nothing because the original node is already added.
add_to	Adds to graph builder. Does nothing because the original node is already added.
and_	Automatically adds operator OnnxAnd to the graph.
and_	Automatically adds operator OnnxAnd to the graph.
astype	Automatically adds operator OnnxCast to the graph.
astype	Automatically adds operator OnnxCast to the graph.
f	Computes the predictions for this node. Similar to an eager evaluation.
f	Evaluates this node.
f	Computes the predictions for this node. Similar to an eager evaluation.
f	Computes the predictions for this node. Similar to an eager evaluation.
f	Evaluates this node.
find_named_inputs	Retrieves all named inputs in this graph.
find_named_inputs	Returns all inputs to the graph.
find_named_inputs	Retrieves all named inputs in this graph.
find_named_inputs	Returns all inputs to the graph.
find_named_inputs	Returns all inputs to the graph.
find_schema	Checks if there is an existing schema for a specific version.
find_schema	Checks if there is an existing schema for a specific version.
get_input_names	Returns input names for node node and inputs inputs.
get_output_result	Returns the output name at position i.
get_output_result	Returns the output name at position i.
get_output_result	Returns the output name at position i.
get_unique_name	Returns a unique name to name an output.
get_unique_output_name	Returns a unique output_name for a NodeResultName.
not_	Automatically adds operator OnnxNot to the graph.
not_	Automatically adds operator OnnxNot to the graph.
or_	Automatically adds operator OnnxOr to the graph.
or_	Automatically adds operator OnnxOr to the graph.
predecessors	Returns the list of predecessors.
predecessors	Returns the list of predecessors.
reserve_name	Reserves a name so that it cannot be changed.
reserve_names	Adds names to the list of reserved names. All must be unique.
run	Other name for OnnxInference.f.
run	Other name for OnnxInference.f.
to_onnx	Converts this operator into an ONNX graph.
to_onnx	Converts this operator into an ONNX graph.
to_onnx	Converts this operator into an ONNX graph. It follows the same signature as OnnxOperator.to_onnx …
to_onnx	Converts this operator into an ONNX graph.
to_onnx_this	Returns a simple ONNX graph corresponding to this node.
to_onnx_this	Returns a simple ONNX graph corresponding to this node.
update_max_item	Some operators return a undefined number of outputs. The method is called when require one of them (with __getitem__) …
update_max_item	Some operators return a undefined number of outputs. The method is called when require one of them (with __getitem__) …

Documentation

Xop API to build onnx graphs. Inspired from sklearn-onnx.

New in version 0.9.

source on GitHub

mlprodict.npy.xop.ClassFactory(class_name, op_name, inputs, outputs, input_range, output_range, domain, attr_names, doc, deprecated, since_version, past_version)

Dynamically creates a class for a specific operator.

Parameters

• class_name – class name

• op_name – operator type

• inputs – expected inputs

• outputs – expected outputs

• input_range – input range

• output_range – output_range

• domain – domain

• attr_names – attributes names

• doc – docstring

• deprecated – is the operator deprecated

• since_version – available since version

• past_version – list of versions

source on GitHub

class mlprodict.npy.xop.OnnxLoadFactory

Bases: object

Automatically creating all operators from onnx packages takes time. That’s why function loadop only creates classes for the requested operators. This class does the same when an attributes is requested.

cl = OnnxLoadOperators()
x = cl.Add(...)

It is equivalent to:

OnnxAdd = loadop('Add')
x = OnnxAdd(...)

source on GitHub

__getattr__(name)

Enables expressions such as:

ops = OnnxLoadFactory()
op = ops.Abs('X')

source on GitHub

__init__()

class mlprodict.npy.xop.OnnxOperator(*inputs, op_version=None, output_names=None, domain=None, global_context=None, **kwargs)

Bases: mlprodict.npy.xop.OnnxOperatorBase

Ancestor to every ONNX operator exposed in mlprodict.npy.xops and mlprodict.npy.xops_ml.

Parameters

• inputs – list of inputs expected by the operator

• op_version – to select a specific version of the operator

• output_names – used defined names for the outputs

• domain – to overwrite the default domain

• global_context – operator If executes one subgraph whose nodes may use one existing output in the current context. If not used in the main graph, these operators are not linked to the output and cannot be retrieved. global_context is a dictionary mapped the subgraph input names to these operators.

• kwargs – additional parameters of the operator

source on GitHub

__abs__()

Automatically adds operator OnnxAbs to the graph.

Parameters

ov – onnx node

Returns

OnnxAbs(self, ov)

source on GitHub

__add__(ov)

Automatically adds operator OnnxAdd to the graph.

Parameters

ov – onnx node

Returns

OnnxAdd(self, ov)

source on GitHub

__call__(*args, function_name=None, function_domain=None, **kwargs)

Creates an instance of class OnnxOperatorFunction. Equivalent to OnnxOperatorFunction(proto, *args, **kwargs).

Parameters

• args – see OnnxOperatorFunction

• function_name – name to be given to the function

• function_domain – function domain, if None, it is given a default value

• kwargs – see OnnxOperatorFunction

Returns

instance of type OnnxOperatorFunction

source on GitHub

classmethod __class_getitem__(opset)

Enables expression cls[opset]. It returns the appropriate class cls_opset. Parameter op_version should be specified.

source on GitHub

__eq__(ov)

Automatically adds operator OnnxEqual to the graph.

Parameters

ov – onnx node

Returns

OnnxEqual(self, ov)

source on GitHub

__getitem__(index)

Returns an accessor to one of the output of this node.

source on GitHub

__gt__(ov)

Automatically adds operator OnnxGreater to the graph.

Parameters

ov – onnx node

Returns

OnnxGreater(self, ov)

source on GitHub

__hash__ = None

__init__(*inputs, op_version=None, output_names=None, domain=None, global_context=None, **kwargs)

__iter__()

Allows expressions such as a, b = OnnxTopK(...).

source on GitHub

__lt__(ov)

Automatically adds operator OnnxLess to the graph.

Parameters

ov – onnx node

Returns

OnnxLess(self, ov)

source on GitHub

__matmul__(ov)

Automatically adds operator OnnxMatMul to the graph.

Parameters

ov – onnx node

Returns

OnnMatMul(self, ov)

source on GitHub

__mod__(ov)

Automatically adds operator OnnxMod to the graph.

Parameters

ov – onnx node

Returns

OnnxMod(self, ov)

source on GitHub

__mul__(ov)

Automatically adds operator OnnxMul to the graph.

Parameters

ov – onnx node

Returns

OnnxMul(self, ov)

source on GitHub

__ne__(ov)

Automatically adds operator OnnxNot x OnnxEqual to the graph.

Parameters

ov – onnx node

Returns

OnnxNot(OnnxEqual(self, ov))

source on GitHub

__pow__(ov)

Automatically adds operator OnnxPow to the graph.

Parameters

ov – onnx node

Returns

OnnPow(self, ov)

source on GitHub

__repr__()

usual

source on GitHub

__sub__(ov)

Automatically adds operator OnnxSub to the graph.

Parameters

ov – onnx node

Returns

OnnxSub(self, ov)

source on GitHub

__truediv__(ov)

Automatically adds operator OnnxDiv to the graph.

Parameters

ov – onnx node

Returns

OnnxDiv(self, ov)

source on GitHub

_check()

static _merge_op_version(n1, n2)

_node_to_graph(other_outputs=None, inputs=None, outputs=None, as_function=False)

Builds a graph as a list of nodes to walk through in that order.

source on GitHub

static _node_to_graph_get_type(node, name=None, outputs=None, outputs_dtype=None)

static _node_to_graph_preprocess_list(inputs)

static _node_to_graph_process_input(inputs, set_inputs, node, inp, new_inputs, new_stack, inputs_dtype, as_function=False)

static _node_to_graph_reorder_by_name(new_inputs, inputs)

_post_process_attributes()

Walks through attributes and replaces them by ONNX values.

source on GitHub

add_to(builder)

Adds to graph builder.

Parameters

builder – instance of _GraphBuilder, it must have a method add_node

source on GitHub

and_(ov)

Automatically adds operator OnnxAnd to the graph.

Parameters

ov – onnx node

Returns

OnnxAnd(self, ov)

source on GitHub

astype(to)

Automatically adds operator OnnxCast to the graph.

Parameters

ov – onnx node

Returns

OnnxCast(self, ov, to=to)

source on GitHub

f(*inputs, verbose=0, fLOG=None, clear_cache=False, runtime=None)

Computes the predictions for this node. Similar to an eager evaluation.

Parameters

• inputs – inputs as dictionary or a list of inputs (see below)

• verbose – display information while predicting

• fLOG – logging function if verbose > 0

• clear_cache – onnx graph is created once unless this parameter is True

• runtime – runtime to use for the evaluation, see OnnxInference

Returns

outputs as a dictionary if the input were given as a dictionary or a single result or a tuple otherwise

The inputs refer to the inputs of the graph. The method walks through all inputs and finds inputs defined as string. It replaces them by the value found in the dictionary. If the inputs are specified in a list, the function retrieves the list of inputs defined as a string and assigns them a value. Logging function can be used to get more insight about it. During the evaluation every node is independently converted into ONNX. The ONNX graph is cached in the class itself.

source on GitHub

find_named_inputs()

Retrieves all named inputs in this graph.

source on GitHub

find_schema(op_version)

Checks if there is an existing schema for a specific version.

Parameters

op_version – requested version

Returns

schema

source on GitHub

get_output_result(i=0)

Returns the output name at position i.

source on GitHub

not_()

Automatically adds operator OnnxNot to the graph.

Parameters

ov – onnx node

Returns

OnnxNot(self, ov)

source on GitHub

or_(ov)

Automatically adds operator OnnxOr to the graph.

Parameters

ov – onnx node

Returns

OnnxOr(self, ov)

source on GitHub

property output_names

Returns self.output_names_.

predecessors()

Returns the list of predecessors.

Returns

list of OnnxOperator

source on GitHub

run(*inputs, verbose=0, fLOG=None, clear_cache=False, runtime=None)

Other name for OnnxInference.f.

source on GitHub

to_onnx(inputs=None, outputs=None, other_outputs=None, target_opset=None, optim=True, verbose=0, run_shape=True, function_name=None, function_domain=None, fLOG=<built-in function print>)

Converts this operator into an ONNX graph.

Parameters

• inputs – information about type, it should not be None

• outputs – information about types, if None, the function will use shape inference to guess the final output type and shape

• other_outputs – additional nodes to consider as graph outputs but not outputs of this particular node

• target_opset – dictionary with target opset per domain, None for the default one

• optim – optimize the model with function onnx_optimisations

• run_shape – in case output shapes are not specify, the function runs function infer_shapes to guess them, False would disable that default behaviour

• verbose – prints information

• function_name – if not None, returns a FunctionProto

• function_domain – in case of a function, declares the function as part of this domain

• fLOG – logging function

:return ONNX stucture

source on GitHub

to_onnx_this(evaluated_inputs)

Returns a simple ONNX graph corresponding to this node.

Parameters

evaluated_inputs – inputs as a list

Returns

ONNX graph

source on GitHub

update_max_item(index)

Some operators return a undefined number of outputs. The method is called when require one of them (with __getitem__) and keeps the greater requested index assuming the node does not output any result beyond that index.

Parameters

index – requested index

source on GitHub

class mlprodict.npy.xop.OnnxOperatorBase

Bases: object

Base class for OnnxOperator, OnnxOperator.

source on GitHub

__init__()

add_to(builder)

This method should be overwritten.

f(*args, **kwargs)

Evaluates this node.

source on GitHub

find_named_inputs()

Returns all inputs to the graph.

source on GitHub

property output_names

This method should be overwritten.

class mlprodict.npy.xop.OnnxOperatorFunction(function_proto, *inputs, output_names=None)

Bases: mlprodict.npy.xop.OnnxOperator

This operator is used to insert existing ONNX function into the ONNX graph being built.

source on GitHub

__init__(function_proto, *inputs, output_names=None)

__repr__()

usual

add_to(builder)

Adds to graph builder.

Parameters

builder – instance of _GraphBuilder, it must have a method add_node

source on GitHub

static attribute_to_value(att)

Converts an attribute into a value using python structures.

source on GitHub

class mlprodict.npy.xop.OnnxOperatorItem(onx_op, index, op_version=None)

Bases: mlprodict.npy.xop.OnnxOperatorBase

Accessor to one of the output returned by a OnnxOperator.

Parameters

• onx_op – OnnxOperator

• index – integer

• op_version – defines the opset version

source on GitHub

__init__(onx_op, index, op_version=None)

__repr__()

usual

__str__()

usual

add_to(builder)

Adds to graph builder. Does nothing because the original node is already added.

Parameters

builder – instance of _GraphBuilder, it must have a method add_node

source on GitHub

f(*inputs, verbose=0, fLOG=None, clear_cache=False, runtime=None)

Computes the predictions for this node. Similar to an eager evaluation.

Parameters

• inputs – inputs as dictionary or a list of inputs (see below)

• verbose – display information while predicting

• fLOG – logging function if verbose > 0

• clear_cache – onnx graph is created once unless this parameter is True

• runtime – runtime to use for the evaluation, see OnnxInference

Returns

outputs as a dictionary if the input were given as a dictionary or a single result or a tuple otherwise

The inputs refer to the inputs of the graph. The method walks through all inputs and finds inputs defined as string. It replaces them by the value found in the dictionary. If the inputs are specified in a list, the function retrieves the list of inputs defined as a string and assigns them a value. Logging function can be used to get more insight about it. During the evaluation every node is independently converted into ONNX. The ONNX graph is cached in the class itself.

source on GitHub

find_named_inputs()

Returns all inputs to the graph.

source on GitHub

get_output_result(i=0)

Returns the output name at position i.

source on GitHub

property inputs

Returns the only inputs in a list.

property output_names

Returns None.

class mlprodict.npy.xop.OnnxOperatorTuple(first, *args)

Bases: mlprodict.npy.xop.OnnxOperatorBase

Class used to return multiple OnnxVar at the same time.

source on GitHub

__getitem__(i)

usual

__init__(first, *args)

__iter__()

Iterates on the outputs.

__len__()

usual

__repr__()

usual

add_to(builder)

Adds to graph builder. Does nothing because the original node is already added.

Parameters

builder – instance of _GraphBuilder, it must have a method add_node

source on GitHub

property inputs

Returns the only inputs in a list.

property output_names

Returns ‘output_names’ of attribute ‘unique’.

property outputs

Returns ‘output_names’ of attribute ‘unique’.

to_onnx(inputs=None, outputs=None, other_outputs=None, target_opset=None, optim=True, verbose=0, run_shape=True)

Converts this operator into an ONNX graph. It follows the same signature as OnnxOperator.to_onnx and calls this method of the unique input object or the first one if there are several. In that case, other inputs in attribute values are moved into container other_outputs.

source on GitHub

class mlprodict.npy.xop._GraphBuilder

Bases: object

Graph builder. It takes a graph structure made with instances of OnnxOperatorBase. The main method is to_onnx.

• initializer: list of initializers to add to the ONNX graph

• node: list of nodes to add to the ONNX graph

• input: list of inputs to add to the ONNX graph

• output: list of inputs to add to the ONNX graph

• opsets: opsets of the ONNX graph

• input_names: dictionary of input names

  {name: InputDetectedVariable}

• node_output_names: memorizes a name for a node output

  when the user did not specify any {(id(node), index): OutputDetectedVariable}

• reserved_names: dictionary { name : (node, index) },

  name which should remain unchanged in the ONNX graph

• names: list of uniques names

• functions: dictionary { domain, name: function_proto }

• function_hashes: dictionary { domain, name: hash of function_proto }

source on GitHub

__init__()

_add_name(name)

_process_io(inputs, input_names)

add_function(function_proto, raise_if_exist=False, check_unique=True, opset=1)

Adds a function to the graph.

Parameters

• function_proto – instance of type FunctionProto

• raise_if_exist – raises an exception if a function of the same name was already added

• check_unique – checks if a function was added twice, it is the same

• opset – opset for the domain the function belongs to

source on GitHub

add_initializer(name, init)

Adds an initializer to the graph.

Parameters

• name – initializer name

• init – initializer to copy

Returns

created intializer

source on GitHub

add_node(op_type, name, inputs, outputs, domain='', opset=None, **attributes)

Adds a node to the graph.

Parameters

• op_type – operator type

• name – node name

• inputs – inputs name list

• outputs – outputs name list

• domain – node domain

• opset – node opset

Returns

created node

source on GitHub

get_input_names(node, inputs)

Returns input names for node node and inputs inputs.

Parameters

• node – node

• inputs – inputs

Returns

name

source on GitHub

get_unique_name(name, reserved=True)

Returns a unique name to name an output.

Parameters

• name – name

• reserved – bypass if the name is a reserved one

Returns

unique name, may be the same if not taken already

source on GitHub

get_unique_output_name(result)

Returns a unique output_name for a NodeResultName.

Parameters

result – instance of NodeResultName

source on GitHub

static number2alpha(index)

Converts a numbers into a string keeping the same alphabetical order.

source on GitHub

reserve_name(node, name, index)

Reserves a name so that it cannot be changed.

Parameters

• node – node or None for an input

• name – name

• index – input index

source on GitHub

reserve_names(node, output_names)

Adds names to the list of reserved names. All must be unique.

Parameters

• node – node or None for an input

• output_names – names of the output

source on GitHub

to_onnx(inputs=None, outputs=None, target_opset=None, run_shape=False, optim=True, function_name=None, function_domain=None, verbose=0)

Converts this operator into an ONNX graph.

Parameters

• inputs – specific inputs (as a dictionary) or default inputs if not specified

• outputs – specific outputs

• target_opset – dictionary with target opset per domain, None for the default one

• run_shape – run shape inference before returning the model

• optim – optimize the model with function onnx_optimisations

• function_name – if not None builds a FunctionProto use this name

• function_domain – in case of a function, declares the function as part of this domain, ‘mlprodict’ if None

• verbose – prints information

Returns

onnx graph

source on GitHub

mlprodict.npy.xop._default_OPSET_TO_IR_VERSION()

Returns the default mapping between opset and ir_version.

<<<

import pprint
from mlprodict.npy.xop import _default_OPSET_TO_IR_VERSION
pprint.pprint(_default_OPSET_TO_IR_VERSION())

>>>

    {1: 3,
     2: 3,
     3: 3,
     4: 3,
     5: 3,
     6: 3,
     7: 3,
     8: 4,
     9: 4,
     10: 5,
     11: 6,
     12: 7,
     13: 7,
     14: 7,
     15: 8,
     16: 8}

source on GitHub

mlprodict.npy.xop._domain_to_class_name(domain)

Converts domain into a name.

Parameters

domain – domain name such as ai.onnx.ml

Returns

string

<<<

from mlprodict.npy.xop import _domain_to_class_name
print(_domain_to_class_name('ai.onnx.ml'))

>>>

    AiOnnxMl

source on GitHub

mlprodict.npy.xop._dynamic_class_creation(operator_names=None, cache=False, include_past=False, verbose=0, fLOG=<built-in function print>)

Automatically generates classes for each of the operators module onnx defines and described at Operators and Operators.

Parameters

• operator_names – list of operators to request or None for all

• cache – extract the documentation from onnx package and saves it on disk it True

• include_past – includes past versions if operator_names is None

• verbose – display some progress

• fLOG – logging function

Returns

list of requested operators as a tuple

source on GitHub

mlprodict.npy.xop._find_operator_domain(name)

Determines the domain of an operator. Raises an exception if not found or if there is an ambiguity.

Parameters

name – operator name

Returns

domain

source on GitHub

mlprodict.npy.xop._populate_schemas()

Populates all schemas.

source on GitHub

mlprodict.npy.xop.loadop(*names, cache=False, verbose=0, fLOG=<built-in function print>)

Dynamically creates a class for a every operator type in the given list.

source on GitHub

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