I have the following tensor

```
x = torch.tensor([[[[ 4, 6],
[12, 10]],
[[20, 22],
[28, 30]]],
[[[ 1, 6],
[13, 15]],
[[16, 18],
[29, 26]]]])
```

with dimensions `(batch_size, channels, height, width)`

. Now I want to apply `torch.cartesian_prod()`

to each element of the batch. I can use `.flatten(start_dim=0)`

to get a one-dimensional tensor for each batch element with shape `(batch_size, channels*height*width)`

. However, `torch.cartesian_prod()`

is only defined for one-dimensional tensors. Is there a workaround to compute the cartesian product for each batch dimension?

Currently I use

```
batch_size = 2
indices = list()
for batch in range(batch_size):
indices.append(torch.cartesian_prod(x[batch], x[batch]))
```

which is not really elegant.