e.g., I can update my MWE to use a non-binary value for the indices I’m looking for:
import torch
from torch.autograd import Variable

blah = Variable( torch.Tensor( [[ 1.1,2.2,3.3],[4.4,5.5,6.6],[7.7,8.8,9.9]] ), requires_grad=True )
some_above10 = Variable( torch.Tensor( [ 11, 12, 13, 2, 3, 4, 14, 15, 16]), requires_grad=False)
inds_above10 = torch.nonzero( some_above10 > 10 )
print( "inds_above10", inds_above10 )
#foo = Variable( torch.LongTensor([0,0,0,2,2,2]), requires_grad=False)
foo = inds_above10 / 3
print( "foo", foo, foo.type() )
bar = torch.index_select( blah, 0, foo ) 
print(bar)

which delivers this output:
inds_above10 Variable containing:
 0
 1
 2
 6
 7
 8
[torch.LongTensor of size (6,1)]

foo Variable containing:
 0
 0
 0
 2
 2
 2
[torch.LongTensor of size (6,1)]
 torch.LongTensor
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-5-544b552e6f58> in <module>()
      9 foo = inds_above10 / 3
     10 print( "foo", foo, foo.type() )
---> 11 bar = torch.index_select( blah, 0, foo )
     12 print(bar)

RuntimeError: invalid argument 3: Index is supposed to be an empty tensor or a vector at /Users/andrew/rosetta/GIT/tmol/src/pytorch/aten/src/TH/generic/THTensorMath.c:261

Would you be willing @albanD to confirm that the MWE fails for you, too?