Suppose that we have a 4-dimensional tensor, for instance

```
import torch
X = torch.rand(2, 3, 4, 4)
tensor([[[[-0.9951, 1.6668, 1.3140, 1.4274],
[ 0.2614, 2.6442, -0.3041, 0.7337],
[-1.2690, 0.0125, -0.3885, 0.0535],
[ 1.5270, -0.1186, -0.4458, 0.1389]],
[[ 0.9125, -1.2998, -0.4277, -0.2688],
[-1.6917, -0.8855, -0.2784, -0.6717],
[ 1.1417, 0.4574, 0.4803, -1.6637],
[ 0.7322, 0.2654, -0.1525, 1.7285]],
[[ 1.8310, -1.5765, 0.1392, 1.3431],
[-0.6641, -1.5090, -0.4893, -1.4110],
[ 0.5875, 0.7528, -0.6482, -0.2547],
[-2.3133, 0.3888, 2.1428, 0.2331]]]])
```

I want to compute the maximum and the minimum values of `X`

over the dimensions 2 and 3, that is, to compute two tensors of size (2,3,1,1), one for the maximum and one for the minimum values of the 4x4 blocks.

I started by trying to do that with `torch.max()`

and `torch.min()`

, but I had no luck. I would expect the `dim`

argument of the above functions to be able to take tuple values, but it can take only an integer. So I don’t know how to proceed.

However, specifically for the maximum values, I decided to use `torch.nn.MaxPool2d()`

with `kernel_size=4`

and `stride=4`

. This indeed did the job:

```
max_pool = nn.MaxPool2d(kernel_size=4, stride=4)
X_max = max_pool(X)
tensor([[[[2.6442]],
[[1.7285]],
[[2.1428]]]])
```

But, afaik, there’s no similar layer for “min”-pooling. Could you please help me on how to compute the minima similarly to the maxima?

Thank you.