# PyTorch tensor advanced indexing

You can specify the corresponding row index as:

```
import torch
x = torch.tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
y = torch.tensor([0, 2, 1])
x[range(x.shape[0]), y]
tensor([1, 6, 8])
```

Advanced indexing in pytorch works just as `NumPy's`

, i.e the indexing arrays are broadcast together across the axes. So you could do as in FBruzzesi's answer.

Though similarly to `np.take_along_axis`

, in pytorch you also have `torch.gather`

, to take values along a specific axis:

```
x.gather(1, y.view(-1,1)).view(-1)
# tensor([1, 6, 8])
```