An eigenvector of an x n matrix A is a nonzero column vector v such that Av = Av, for some scalar A called the eigenvalue of v. You will shortly learn more about these in Algebra 2, and in the vector calculus part of Methods 2. Find eigenvectors and eigenvalues of a given matrix is an important problem. There's a simple method which will often produce a sequence of vectors which converge to an eigenvector of a square matrix A: begin with an initial guess bo and form the sequence 1 bn+1 Abn || Abn|| X1 k Here for a column vector x = the scalar ||x|| is defined to be V i=1 Xk Write a function approximate_eigenvector (A, b0, n) which returns the nth term b of the above sequence when the initial guess is be. You can assume that A is a Numpy array of shape (k, k) for some k, that b is a shape (k, 1) column vector, and that n is a positive integer. Your function needs to work for all values of k.