The decision function of support vector machine f(x) can be expressed as f(x)=1, if ; otherwise f(x)=0. x is a test sample whose dimensionality is d, and {x_i} are support vectors automatically selected from the training set. K(x,x_i) is the kernel function to measure the similarity between x and x_i. In this problem, we assume . Show that this support vector machine can be implemented with a 3-layer neural network. Show the network structure, weights, and nonlinear activation functions at each layer.