# Source code for mars.tensor.arithmetic.sqrt

```
#!/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 TensorUnaryOp
from .utils import arithmetic_operand
@arithmetic_operand(sparse_mode="unary")
class TensorSqrt(TensorUnaryOp):
_op_type_ = OperandDef.SQRT
_func_name = "sqrt"
[docs]@infer_dtype(np.sqrt)
def sqrt(x, out=None, where=None, **kwargs):
"""
Return the positive square-root of an tensor, element-wise.
Parameters
----------
x : array_like
The values whose square-roots are required.
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
An tensor of the same shape as `x`, containing the positive
square-root of each element in `x`. If any element in `x` is
complex, a complex tensor is returned (and the square-roots of
negative reals are calculated). If all of the elements in `x`
are real, so is `y`, with negative elements returning ``nan``.
If `out` was provided, `y` is a reference to it.
Notes
-----
*sqrt* has--consistent with common convention--as its branch cut the
real "interval" [`-inf`, 0), and is continuous from above on it.
A branch cut is a curve in the complex plane across which a given
complex function fails to be continuous.
Examples
--------
>>> import mars.tensor as mt
>>> mt.sqrt([1,4,9]).execute()
array([ 1., 2., 3.])
>>> mt.sqrt([4, -1, -3+4J]).execute()
array([ 2.+0.j, 0.+1.j, 1.+2.j])
>>> mt.sqrt([4, -1, mt.inf]).execute()
array([ 2., NaN, Inf])
"""
op = TensorSqrt(**kwargs)
return op(x, out=out, where=where)
```