I have a pytorch sparse tensor that I need sliced row/column wise using this slice [idx][:,idx] where idx is a list of indexes, using the mentioned slice yields my desired result on an ordinary float tensor. Is it possible applying the same slicing on a sparse tensor? Example here:

```
#constructing sparse matrix
i = np.array([[0,1,2,2],[0,1,2,1]])
v = np.ones(4)
i = torch.from_numpy(i.astype("int64"))
v = torch.from_numpy(v.astype("float32"))
test1 = torch.sparse.FloatTensor(i, v)
#constructing float tensor
test2 = np.array([[1,0,0],[0,1,0],[0,1,1]])
test2 = autograd.Variable(torch.cuda.FloatTensor(test2), requires_grad=False)
#slicing
idx = [1,2]
print(test2[idx][:,idx])
```

output:

```
Variable containing:
1 0
1 1
[torch.cuda.FloatTensor of size 2x2 (GPU 0)]
```

I am holding a 250.000 x 250.000 adjacency matrix, where I need to slice 5000 rows and 5000 columns, using the random idx, by simply sampling 5000 random idx’s. Since the dataset is so large it is not realistic to convert to a more convenient datatype.

can I achieve the same slicing result on test1? Is it even possible? If not, are there any work-arounds?