# tensorplay.functional.remainder

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.remainder.html

```python
tensorplay.functional.remainder(input, other, *, out=None) → Tensor
```

Computes
[Python’s modulus operation](https://docs.python.org/3/reference/expressions.html#binary-arithmetic-operations)
entrywise.  The result has the same sign as the divisor other and its absolute value
is less than that of other.

It may also be defined in terms of tensorplay.div() as

```
tensorplay.remainder(a, b) == a - a.div(b, rounding_mode="floor") * b
```

Supports [broadcasting to a common shape](/docs/notes/broadcasting.html#broadcasting-semantics),
type promotion, and integer and float inputs.

> **Note**
>
> Complex inputs are not supported. In some cases, it is not mathematically possible to satisfy the definition of a modulo operation with complex numbers. See tensorplay.fmod() for how division by zero is handled.

> **See also**
>
> tensorplay.fmod() which implements C++’s std::fmod. This one is defined in terms of division rounding towards zero.

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor) or [Scalar](/docs/generated/tensorplay.Scalar.html#tensorplay.Scalar)) – the dividend

- other ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor) or [Scalar](/docs/generated/tensorplay.Scalar.html#tensorplay.Scalar)) – the divisor

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> tensorplay.remainder(tensorplay.tensor([-3., -2, -1, 1, 2, 3]), 2)
tensor([ 1.,  0.,  1.,  1.,  0.,  1.])
>>> tensorplay.remainder(tensorplay.tensor([1, 2, 3, 4, 5]), -1.5)
tensor([ -0.5000, -1.0000,  0.0000, -0.5000, -1.0000 ])
```