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Merge pull request #477 from bilgin-kocak/feat-bitwise-xor
Feat bitwise xor
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#tensor.bitwise_xor | ||
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```rust | ||
fn bitwise_xor(self: @Tensor<T>, other: @Tensor<T>) -> Tensor<usize>; | ||
``` | ||
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Computes the bitwise XOR of two tensors element-wise. | ||
The input tensors must have either: | ||
* Exactly the same shape | ||
* The same number of dimensions and the length of each dimension is either a common length or 1. | ||
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## Args | ||
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* `self`(`@Tensor<T>`) - The first tensor to be compared | ||
* `other`(`@Tensor<T>`) - The second tensor to be compared | ||
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## Panics | ||
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* Panics if the shapes are not equal or broadcastable | ||
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## Returns | ||
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A new `Tensor<T>` with the same shape as the broadcasted inputs. | ||
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## Example | ||
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```rust | ||
use array::{ArrayTrait, SpanTrait}; | ||
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use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor}; | ||
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fn xor_example() -> Tensor<usize> { | ||
let tensor_1 = TensorTrait::<u32>::new( | ||
shape: array![3, 3].span(), data: array![0, 1, 2, 3, 4, 5, 6, 7, 8].span(), | ||
); | ||
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let tensor_2 = TensorTrait::<u32>::new( | ||
shape: array![3, 3].span(), data: array![0, 1, 2, 0, 4, 5, 0, 6, 2].span(), | ||
); | ||
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return tensor_1.bitwise_xor(@tensor_2); | ||
} | ||
>>> [0,0,0,3,0,0,6,1,10] | ||
``` |
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