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paddle. gcd ( x: Tensor, y: Tensor, name: str | None = None ) → Tensor [source]
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Computes the element-wise greatest common divisor (GCD) of input |x| and |y|. Both x and y must have integer types.

Note

gcd(0,0)=0, gcd(0, y)=|y|

If x.shape != y.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

Parameters

  • x (Tensor) – An N-D Tensor, the data type is int32, int64.

  • y (Tensor) – An N-D Tensor, the data type is int32, int64.

  • name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

An N-D Tensor, the data type is the same with input.

Return type

out (Tensor)

Examples

>>> import paddle

>>> x1 = paddle.to_tensor(12)
>>> x2 = paddle.to_tensor(20)
>>> paddle.gcd(x1, x2)
Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True,
4)

>>> x3 = paddle.arange(6)
>>> paddle.gcd(x3, x2)
Tensor(shape=[6], dtype=int64, place=Place(cpu), stop_gradient=True,
[20, 1 , 2 , 1 , 4 , 5])

>>> x4 = paddle.to_tensor(0)
>>> paddle.gcd(x4, x2)
Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True,
20)

>>> paddle.gcd(x4, x4)
Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True,
0)

>>> x5 = paddle.to_tensor(-20)
>>> paddle.gcd(x1, x5)
Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True,
4)