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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 jacobi_ne(A, x, b, iterations=1, omega=1.0):
 """Perform Jacobi iterations on the linear system A A.H x = A.H b. Also known as Cimmino relaxation Parameters A : csr_matrix Sparse NxN matrix x : ndarray Approximate solution (length N) b : ndarray Right-hand side (length N) iterations : int Number of iterations to perform omega : scalar Damping parameter Returns ------- Nothing, x will be modified in place. References .. [1] Brandt, Ta'asan. "Multigrid Method For Nearly Singular And Slightly Indefinite Problems." 1985. NASA Technical Report Numbers: ICASE-85-57; NAS 1.26:178026; NASA-CR-178026; .. [2] Kaczmarz. Angenaeherte Aufloesung von Systemen Linearer Gleichungen. Bull. Acad. Polon. Sci. Lett. A 35, 355-57. 1937 .. [3] Cimmino. La ricerca scientifica ser. II 1. Pubbliz. dell'Inst. pre le Appl. del Calculo 34, 326-333, 1938. Examples -------- 49.3886046066 """

    A, x, b = make_system(A, x, b, formats=['csr'])

    sweep = slice(None)
    (row_start, row_stop, row_step) = sweep.indices(A.shape[0])

    temp = np.zeros_like(x)

    # Dinv for A*A.H
    Dinv = get_diagonal(A, norm_eq=2, inv=True)

    # Create uniform type, convert possibly complex scalars to length 1 arrays
    [omega] = type_prep(A.dtype, [omega])

    for i in range(iterations):
        delta = (np.ravel(b - A*x)*np.ravel(Dinv)).astype(A.dtype)
        amg_core.jacobi_ne(A.indptr, A.indices, A.data,
                           x, b, delta, temp, row_start,
                           row_stop, row_step, omega)