# tensorplay.functional.fmin

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

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

Computes the element-wise minimum of input and other.

This is like tensorplay.minimum() 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 minimum.
Only if both elements are NaN is NaN propagated.

This function is a wrapper around C++’s std::fmin and is similar to NumPy’s fmin 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([2.2, float('nan'), 2.1, float('nan')])
>>> b = tensorplay.tensor([-9.3, 0.1, float('nan'), float('nan')])
>>> tensorplay.fmin(a, b)
tensor([-9.3000, 0.1000, 2.1000,    nan])
```