Hi, I am using torch 1.7.1 and I noticed that vectorized sums are different from sums in a loop if the indices are repeated. For example:

```
import torch
indices = torch.LongTensor([0,1,2,1])
values = torch.FloatTensor([1,1,2,2])
result = torch.FloatTensor([0,0,0])
looped_result = torch.zeros_like(result)
for i in range(indices.shape[0]):
looped_result[indices[i]] += values[i]
result[indices] += values
print('result',result)
print('looped result', looped_result)
```

results in:

```
>> result tensor([1., 2., 2.])
>> looped result tensor([1., 3., 2.])
```

As you can see the looped variable has the correct sums while the vectorized one doesnâ€™t. Is it possible to avoid the loop and still get the correct result?