# tensorplay.functional.fmax

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

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

Computes the element-wise maximum of input and other.

This is like tensorplay.maximum() except it handles NaNs differently:
if exactly one of the two elements being compared is a NaN then the non-NaN element is taken as the maximum.
Only if both elements are NaN is NaN propagated.

This function is a wrapper around C++’s std::fmax and is similar to NumPy’s fmax function.

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

Parameters:

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

- other ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the second input tensor

Keyword Arguments:

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

Example:

```
>>> a = tensorplay.tensor([9.7, float('nan'), 3.1, float('nan')])
>>> b = tensorplay.tensor([-2.2, 0.5, float('nan'), float('nan')])
>>> tensorplay.fmax(a, b)
tensor([9.7000, 0.5000, 3.1000,    nan])
```