Solved by magz in post #3 
              
              
                you could use convolution kernels of size 1. 
transform your input to a 1-dimensional spatial input and “spread” the 12 features along the channel dimensions. (x = x.view(1,12,1) (batch, channel, space)) 
then you define a 1D-convolution with bias set to True with 12 input channels and 12 output cha…