module onnxrt.ops_cpu.op_pow
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Short summary#
module mlprodict.onnxrt.ops_cpu.op_pow
Runtime operator.
Classes#
class |
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Pow === Pow takes input data (Tensor<T>) and exponent Tensor, and produces one output data (Tensor<T>) where the function … |
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_pow.Pow(onnx_node, desc=None, **options)#
Bases:
OpRun
===
Pow takes input data (Tensor<T>) and exponent Tensor, and produces one output data (Tensor<T>) where the function f(x) = x^exponent, is applied to the data tensor elementwise. This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.
Inputs
X (heterogeneous)T: First operand, base of the exponent.
Y (heterogeneous)T1: Second operand, power of the exponent.
Outputs
Z (heterogeneous)T: Output tensor
Type Constraints
T tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16): Constrain input X and output types to float/int tensors.
T1 tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16): Constrain input Y types to float/int tensors.
Version
Onnx name: Pow
This version of the operator has been available since version 15.
Runtime implementation:
Pow
- __init__(onnx_node, desc=None, **options)#
- _run(a, b, attributes=None, verbose=0, fLOG=None)#
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