# Source code for mars.tensor.arithmetic.subtract

```
#!/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_and")
class TensorSubtract(TensorBinOp):
_op_type_ = OperandDef.SUB
_func_name = "subtract"
[docs]@infer_dtype(np.subtract)
def subtract(x1, x2, out=None, where=None, **kwargs):
"""
Subtract arguments, element-wise.
Parameters
----------
x1, x2 : array_like
The tensors to be subtracted from each other.
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
The difference of `x1` and `x2`, element-wise. Returns a scalar if
both `x1` and `x2` are scalars.
Notes
-----
Equivalent to ``x1 - x2`` in terms of tensor broadcasting.
Examples
--------
>>> import mars.tensor as mt
>>> mt.subtract(1.0, 4.0).execute()
-3.0
>>> x1 = mt.arange(9.0).reshape((3, 3))
>>> x2 = mt.arange(3.0)
>>> mt.subtract(x1, x2).execute()
array([[ 0., 0., 0.],
[ 3., 3., 3.],
[ 6., 6., 6.]])
"""
op = TensorSubtract(**kwargs)
return op(x1, x2, out=out, where=where)
@infer_dtype(np.subtract, reverse=True)
def rsubtract(x1, x2, **kwargs):
op = TensorSubtract(**kwargs)
return op.rcall(x1, x2)
```