未验证 提交 698fca80 编写于 作者: T Tao Luo 提交者: GitHub

refine gcd & lcm docs (#38029)

上级 65494051
......@@ -2796,9 +2796,11 @@ def gcd(x, y, name=None):
Note:
gcd(0,0)=0, gcd(0, y)=|y|
Args:
x, y (Tensor): An N-D Tensor, the data type is int8,int16,int32,int64,uint8.
If x.shape != y.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
Args:
x (Tensor): An N-D Tensor, the data type is int8,int16,int32,int64,uint8.
y (Tensor): An N-D Tensor, the data type is int8,int16,int32,int64,uint8.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
......@@ -2808,7 +2810,6 @@ def gcd(x, y, name=None):
.. code-block:: python
import paddle
import numpy as np
x1 = paddle.to_tensor(12)
x2 = paddle.to_tensor(20)
......@@ -2816,7 +2817,7 @@ def gcd(x, y, name=None):
# Tensor(shape=[1], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [4])
x3 = paddle.to_tensor(np.arange(6))
x3 = paddle.arange(6)
paddle.gcd(x3, x2)
# Tensor(shape=[6], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [20, 1 , 2 , 1 , 4 , 5])
......@@ -2873,9 +2874,11 @@ def lcm(x, y, name=None):
Note:
lcm(0,0)=0, lcm(0, y)=0
Args:
x, y (Tensor): An N-D Tensor, the data type is int8,int16,int32,int64,uint8.
If x.shape != y.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
Args:
x (Tensor): An N-D Tensor, the data type is int8,int16,int32,int64,uint8.
y (Tensor): An N-D Tensor, the data type is int8,int16,int32,int64,uint8.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
......@@ -2885,7 +2888,6 @@ def lcm(x, y, name=None):
.. code-block:: python
import paddle
import numpy as np
x1 = paddle.to_tensor(12)
x2 = paddle.to_tensor(20)
......@@ -2893,7 +2895,7 @@ def lcm(x, y, name=None):
# Tensor(shape=[1], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [60])
x3 = paddle.to_tensor(np.arange(6))
x3 = paddle.arange(6)
paddle.lcm(x3, x2)
# Tensor(shape=[6], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [0, 20, 20, 60, 20, 20])
......
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