module = JoinTable(dimension)

Creates a module that takes a list of Tensors as input and outputs a Tensor by joining them together along dimension dimension.

Example: 

require "lab"
x=lab.randn(5,1)
y=lab.randn(5,1)
z=lab.randn(2,1)

print(nn.JoinTable(1):forward{x,y})
print(nn.JoinTable(2):forward{x,y})
print(nn.JoinTable(1):forward{x,z})
gives the output: 
1.3965
 0.5146
-1.5244
-0.9540
 0.4256
 0.1575
 0.4491
 0.6580
 0.1784
-1.7362
 
 1.3965  0.1575
 0.5146  0.4491
-1.5244  0.6580
-0.9540  0.1784
 0.4256 -1.7362

 1.3965
 0.5146
-1.5244
-0.9540
 0.4256
-1.2660
 1.0869
[torch.Tensor of dimension 7x1]

A more complicated example: 

require "lab"

mlp=nn.Sequential();       --Create a network that takes a Tensor as input
 c=nn.ConcatTable()        --The same Tensor goes through two different Linear
 c:add(nn.Linear(10,3))	   --Layers in Parallel
 c:add(nn.Linear(10,7))
mlp:add(c)                 --Outputing a table with 2 elements
 p=nn.ParallelTable()      --These tables go through two more linear layers
 p:add(nn.Linear(3,2))	   -- separately.
 p:add(nn.Linear(7,1)) 
mlp:add(p) 
mlp:add(nn.JoinTable(1))   --Finally, the tables are joined together and output. 

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

for i=1,100 do             -- A few steps of training such a network.. 
 x=lab.ones(10);
 y=torch.Tensor(3); y:copy(x:narrow(1,1,3))
 pred=mlp:forward(x)

 criterion= nn.MSECriterion()
 local err=criterion:forward(pred,y)
 local gradCriterion = criterion:backward(pred,y);
 mlp:zeroGradParameters();
 mlp:backward(x, gradCriterion); 
 mlp:updateParameters(0.05);

 print(err)
end