Pow - 12 vs 13¶
- Pow12 → Pow13 +2 -1
Pow12 → Pow13
RENAMED
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@@ -1 +1 @@
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1
1
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Pow takes input data (Tensor<T>) and exponent Tensor, and
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2
2
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produces one output data (Tensor<T>) where the function f(x) = x^exponent,
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3
3
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is applied to the data tensor elementwise.
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4
4
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This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check Broadcasting in ONNX <https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md>_.
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5
5
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**Inputs**
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6
6
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* **X** (heterogeneous) - **T**:
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7
7
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First operand, base of the exponent.
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8
8
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* **Y** (heterogeneous) - **T1**:
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9
9
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Second operand, power of the exponent.
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10
10
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**Outputs**
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11
11
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* **Z** (heterogeneous) - **T**:
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12
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-
Output tensor
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12
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+
Output tensor
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13
13
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**Type Constraints**
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14
14
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* **T** in (
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15
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+
tensor(bfloat16),
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15
16
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tensor(double),
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16
17
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tensor(float),
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17
18
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tensor(float16),
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18
19
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tensor(int32),
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19
20
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tensor(int64)
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20
21
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):
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21
22
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Constrain input X and output types to float/int tensors.
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22
23
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* **T1** in (
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23
24
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tensor(double),
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24
25
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tensor(float),
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25
26
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tensor(float16),
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26
27
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tensor(int16),
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27
28
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tensor(int32),
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28
29
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tensor(int64),
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29
30
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tensor(int8),
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30
31
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tensor(uint16),
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31
32
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tensor(uint32),
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32
33
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tensor(uint64),
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33
34
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tensor(uint8)
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34
35
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):
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35
36
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Constrain input Y types to float/int tensors.
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