Suppose you have a neural network with 3 layers. The first layer is the input layer with 20 neurons just encoding the 20 input binary values (0 or 1). The second layer consists of 30 perceptrons each with their own weights and biases. The final layer is just one output perceptron with its weights and bias. The neurons in adjacent layers are fully connected. So this neural network computes a function y=f(x₁,x₂,,x₂₀) where all variables take binary values. Assume that wx+b is never 0 for every neuron in the network and every possible input x. Is it possible to change the weights and biases of the perceptrons in this neural network in a systematic way so that the new neural network computes a new function z=h(x₁,x₂,,x₂₀) such that h(x₁,x₂,,x₂₀)=1- f(x₁,x₂,,x₂₀) for all possible inputs x₁,x₂,,x₂₀? If so, how?