mindspore.ops.bitwise_xor

mindspore.ops.bitwise_xor(x, y)[source]

Returns bitwise xor of two tensors element-wise.

\[out_i = x_{i} \oplus y_{i}\]

Args of x and y comply with the implicit type conversion rules to make the data types consistent. If they have different data types, the lower priority data type will be converted to the relatively highest priority data type.

Parameters

x (Tensor) – The first input tensor with shape \((N,*)\) where \(*\) means any number of additional dimensions. The supported data types are: int8, uint8, int16, uint16, int32, uint32, int64 and uint64.
y (Tensor) – The second input tensor with the same dtype as x.

Returns

Tensor, has the same type as the x.

Raises

TypeError – If x or y is not a Tensor.

Supported Platforms:: Ascend GPU CPU

Examples

>>> x = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16)
>>> y = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16)
>>> output = ops.bitwise_xor(x, y)
>>> print(output)
[ 0  1  0  0 -2  3  2]