Stage 3's neuron, one forward and backward: implement relu(v) (gated forward AND gated gradient), then compute pred = relu(wx + b), err = pred - Value(target), loss = errerr, and backpropagate. Print f"pred={pred.data} loss={loss.data}" and f"dL/dw={w.grad} dL/db={b.grad}".