# Source code for mars.tensor.arithmetic.logical_xor

```#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import numpy as np

from ... import opcodes as OperandDef
from ..utils import infer_dtype
from .core import TensorBinOp
from .utils import arithmetic_operand

@arithmetic_operand(sparse_mode="binary_and")
class TensorXor(TensorBinOp):
_op_type_ = OperandDef.XOR
_func_name = "logical_xor"

[docs]@infer_dtype(np.logical_xor)
def logical_xor(x1, x2, out=None, where=None, **kwargs):
"""
Compute the truth value of x1 XOR x2, element-wise.

Parameters
----------
x1, x2 : array_like
Logical XOR is applied to the elements of `x1` and `x2`.  They must
be broadcastable to the same shape.
out : Tensor, None, or tuple of Tensor and None, optional
A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or `None`,
a freshly-allocated tensor is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values
of False indicate to leave the value in the output alone.
**kwargs

Returns
-------
y : bool or Tensor of bool
Boolean result of the logical XOR operation applied to the elements
of `x1` and `x2`; the shape is determined by whether or not
broadcasting of one or both arrays was required.

--------
logical_and, logical_or, logical_not, bitwise_xor

Examples
--------
>>> import mars.tensor as mt

>>> mt.logical_xor(True, False).execute()
True
>>> mt.logical_xor([True, True, False, False], [True, False, True, False]).execute()
array([False,  True,  True, False])

>>> x = mt.arange(5)
>>> mt.logical_xor(x < 1, x > 3).execute()
array([ True, False, False, False,  True])

Simple example showing support of broadcasting

>>> mt.logical_xor(0, mt.eye(2)).execute()
array([[ True, False],
[False,  True]])
"""
op = TensorXor(**kwargs)
return op(x1, x2, out=out, where=where)

@infer_dtype(np.logical_xor, reverse=True)
def rlogical_xor(x1, x2, **kwargs):
op = TensorXor(**kwargs)
return op.rcall(x1, x2)
```