Selects a dimension and index of a nxpxqx.. Tensor.

Example: 

mlp=nn.Sequential();
mlp:add(nn.Select(1,3))

require "lab"
x=lab.randn(10,5)
print(x)
print(mlp:forward(x))
gives the output: 
 0.9720 -0.0836  0.0831 -0.2059 -0.0871
 0.8750 -2.0432 -0.1295 -2.3932  0.8168
 0.0369  1.1633  0.6483  1.2862  0.6596
 0.1667 -0.5704 -0.7303  0.3697 -2.2941
 0.4794  2.0636  0.3502  0.3560 -0.5500
-0.1898 -1.1547  0.1145 -1.1399  0.1711
-1.5130  1.4445  0.2356 -0.5393 -0.6222
-0.6587  0.4314  1.1916 -1.4509  1.9400
 0.2733  1.0911  0.7667  0.4002  0.1646
 0.5804 -0.5333  1.1621  1.5683 -0.1978
[torch.Tensor of dimension 10x5]

 0.0369
 1.1633
 0.6483
 1.2862
 0.6596
[torch.Tensor of dimension 5]

This can be used in conjunction with Concat to emulate the behavior of Parallel, or to select various parts of an input Tensor to perform operations on. Here is a fairly complicated example: 

require "lab"

mlp=nn.Sequential();
c=nn.Concat(2) 
for i=1,10 do
 local t=nn.Sequential()
 t:add(nn.Select(1,i))
 t:add(nn.Linear(3,2)) 
 t:add(nn.Reshape(2,1))
 c:add(t)
end
mlp:add(c)

pred=mlp:forward(lab.randn(10,3))
print(pred)

for i=1,10000 do     -- Train for a few iterations
 x=lab.randn(10,3);
 y=lab.ones(2,10);
 pred=mlp:forward(x)

 criterion= nn.MSECriterion()
 err=criterion:forward(pred,y)
 gradCriterion = criterion:backward(pred,y);
 mlp:zeroGradParameters();
 mlp:backward(x, gradCriterion); 
 mlp:updateParameters(0.01);
 print(err)
end