Say we have two features x1 and x2.
I want the neural network to give the same results when I switch the two features.
A unstrict way would be  argument the data by switching two columns. But this won’t guarantee symmetry.
This neural network could be multi-layers and there could be other features which I don’t want to enforce any constraints.
Edit: My guessing is I could generate a layer purely for the two features, e.g, a Linear(1,10), using this to act on both of them. And use another linear layer to act on other features. I could then concat or add the next layer. Give me suggestions if there are better practice. Thanks！