# tensorplay.functional.fmod

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

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

Applies C++’s [std::fmod](https://en.cppreference.com/w/cpp/numeric/math/fmod) entrywise.
The result has the same sign as the dividend input and its absolute value
is less than that of other.

This function may be defined in terms of tensorplay.div() as

```
tensorplay.fmod(a, b) == a - a.div(b, rounding_mode="trunc") * b
```

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

> **Note**
>
> When the divisor is zero, returns NaN for floating point dtypes on both CPU and GPU; raises RuntimeError for integer division by zero on CPU; Integer division by zero on GPU may return any value.

> **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 also**
>
> tensorplay.remainder() which implements Python’s modulus operator. This one is defined using division rounding down the result.

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – 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.fmod(tensorplay.tensor([-3., -2, -1, 1, 2, 3]), 2)
tensor([-1., -0., -1.,  1.,  0.,  1.])
>>> tensorplay.fmod(tensorplay.tensor([1, 2, 3, 4, 5]), -1.5)
tensor([1.0000, 0.5000, 0.0000, 1.0000, 0.5000])
```