Next, implement a function called multiply_matrices_vectorized that also receives matrices A and B as input and returns C = A x B. Similarly to function multiply_matrices, you can assume that the multiplication is possible for A and B. In contrast with function multiply_matrices, your implementation of multiply_matrices_vectorized should contain only two 'for' loops; the innermost 'for' should be replaced by a vectorized implementation where you will use slicing, the operator * for two vectors, and the function sum from Numpy. def multiply_matrices_vectorized(A, B): C = np.zeros((A. shape[o], B.shape[1])) # implement here your vectorized solution return C C = multiply_matrices_vectorized (A, B) print('C = A x B') print('C = '). print(C)