I have a `(N, V)`

matrix with predictions. Now I want to index this matrix with a `(N, C)`

matrix to obtain a `(N,C)`

matrix with the predictions corresponding to the indices of the second matrix.

Example:

```
a = torch.FloatTensor([[0.1, 0.3, 0.5, 0.1],
[0.2, 0.2, 0.3, 0.3]])
b = torch.LongTensor([[0, 1],
[2, 3]])
```

I want to do something like `c = a[b]`

to obtain

```
tensor([[0.1, 0.3],
[0.3, 0.3]])
```

I tried `a.take(b)`

but this returns only values from the first row of `a`

(ie. `[[0.1, 0.3], [0.5, 0.1]]`

EDIT:

The following does the job, but I don’t know if it messes with the computation graph.

```
c = Variable(torch.empty_like(b).float(), requires_grad=True)
for i in range(a.size(0)):
c[i, :] = a[i, b[i]]
```