module onnxrt.ops_cpu.op_xor
#
Short summary#
module mlprodict.onnxrt.ops_cpu.op_xor
Runtime operator.
Classes#
class |
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Xor === Returns the tensor resulted from performing the xor logical operation elementwise on the input tensors A and … |
Properties#
property |
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Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
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Returns the list of modified parameters. |
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Returns the list of optional arguments. |
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Returns the list of optional arguments. |
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Returns all parameters in a dictionary. |
Methods#
method |
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Documentation#
Runtime operator.
- class mlprodict.onnxrt.ops_cpu.op_xor.Xor(onnx_node, desc=None, **options)#
Bases:
mlprodict.onnxrt.ops_cpu._op.OpRunBinary
===
Returns the tensor resulted from performing the xor logical operation elementwise on the input tensors A and B (with Numpy-style broadcasting support).
This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.
Inputs
A (heterogeneous)T: First input operand for the logical operator.
B (heterogeneous)T: Second input operand for the logical operator.
Outputs
C (heterogeneous)T1: Result tensor.
Type Constraints
T tensor(bool): Constrains input to boolean tensor.
T1 tensor(bool): Constrains output to boolean tensor.
Version
Onnx name: Xor
This version of the operator has been available since version 7.
Runtime implementation:
Xor
- Parameters
onnx_node – onnx node
desc – internal representation
expected_attributes – expected attributes for this node
options – runtime options
- __init__(onnx_node, desc=None, **options)#
- Parameters
onnx_node – onnx node
desc – internal representation
expected_attributes – expected attributes for this node
options – runtime options
- _run(a, b)#
Should be overwritten.
- to_python(inputs)#
Returns a python code equivalent to this operator.
- Parameters
inputs – inputs name
- Returns
imports, python code, both as strings