# Source code for mars.tensor.arithmetic.logical_and

```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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_or")
class TensorAnd(TensorBinOp):
_op_type_ = OperandDef.AND
_func_name = "logical_and"
[docs]@infer_dtype(np.logical_and)
def logical_and(x1, x2, out=None, where=None, **kwargs):
"""
Compute the truth value of x1 AND x2 element-wise.
Parameters
----------
x1, x2 : array_like
Input tensors. `x1` and `x2` must 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
AND operation on corresponding elements of `x1` and `x2`.
See Also
--------
logical_or, logical_not, logical_xor
bitwise_and
Examples
--------
>>> import mars.tensor as mt
>>> mt.logical_and(True, False).execute()
False
>>> mt.logical_and([True, False], [False, False]).execute()
array([False, False])
>>> x = mt.arange(5)
>>> mt.logical_and(x>1, x<4).execute()
array([False, False, True, True, False])
"""
op = TensorAnd(**kwargs)
return op(x1, x2, out=out, where=where)
@infer_dtype(np.logical_and, reverse=True)
def rlogical_and(x1, x2, **kwargs):
op = TensorAnd(**kwargs)
return op.rcall(x1, x2)
```