Suppose we have a Support Vector Machine (SVM) kernel K(x, y) such that there is an implicit high-dimensional feature map F: d D that satisfies K(x, y) = F(x) F(y). Show how to calculate the Euclidean distance in the D-dimensional space without explicitly calculating the values in the D-dimensional vectors, i.e., without first computing F(x) and F(y).