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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_maps(A):
 """Get mappings from the square array A to the flat vector of parameters alpha. Helper function for PCCA+ optimization. Parameters A : ndarray The transformation matrix A. Returns ------- flat_map : ndarray Mapping from flat indices (k) to square (i,j) indices. square map : ndarray Mapping from square indices (i,j) to flat indices (k). """


    N = A.shape[0]
    flat_map = []
    for i in range(1, N):
        for j in range(1, N):
            flat_map.append([i, j])

    flat_map = np.array(flat_map)

    square_map = np.zeros(A.shape, 'int')

    for k in range((N - 1) ** 2):
        i, j = flat_map[k]
        square_map[i, j] = k

    return flat_map, square_map