# Source code for mars.tensor.arithmetic.logical_or

```#!/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 TensorOr(TensorBinOp):
_op_type_ = OperandDef.OR
_func_name = "logical_or"

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

Parameters
----------
x1, x2 : array_like
Logical OR is applied to the elements of `x1` and `x2`.
They have to be of 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 : Tensor or bool
Boolean result with the same shape as `x1` and `x2` of the logical
OR operation on elements of `x1` and `x2`.

--------
logical_and, logical_not, logical_xor
bitwise_or

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

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

>>> x = mt.arange(5)
>>> mt.logical_or(x < 1, x > 3).execute()
array([ True, False, False, False,  True])
"""
op = TensorOr(**kwargs)
return op(x1, x2, out=out, where=where)

@infer_dtype(np.logical_or, reverse=True)
def rlogical_or(x1, x2, **kwargs):
op = TensorOr(**kwargs)
return op.rcall(x1, x2)
```