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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 bicgstab(A, b, x0=None, tol=1e-5, maxiter=None, xtype=None, M=None, callback=None, residuals=None):
 """Biconjugate Gradient Algorithm with Stabilization. Solves the linear system Ax = b. Left preconditioning is supported. Parameters A : array, matrix, sparse matrix, LinearOperator n x n, linear system to solve b : array, matrix right hand side, shape is (n,) or (n,1) x0 : array, matrix initial guess, default is a vector of zeros tol : float relative convergence tolerance, i.e. tol is scaled by ||r_0||_2 maxiter : int maximum number of allowed iterations xtype : type dtype for the solution, default is automatic type detection M : array, matrix, sparse matrix, LinearOperator n x n, inverted preconditioner, i.e. solve M A A.H x = M b. callback : function User-supplied function is called after each iteration as callback(xk), where xk is the current solution vector residuals : list residuals has the residual norm history, including the initial residual, appended to it Returns ------- (xNew, info) xNew : an updated guess to the solution of Ax = b info : halting status of bicgstab == ====================================== 0 successful exit >0 convergence to tolerance not achieved, return iteration count instead. <0 numerical breakdown, or illegal input == ====================================== Notes ----- The LinearOperator class is in scipy.sparse.linalg.interface. Use this class if you prefer to define A or M as a mat-vec routine as opposed to explicitly constructing the matrix. A.psolve(..) is still supported as a legacy. Examples -------- 4.68163045309 References .. [1] Yousef Saad, "Iterative Methods for Sparse Linear Systems, Second Edition", SIAM, pp. 231-234, 2003 http://www-users.cs.umn.edu/~saad/books.html """

    # Convert inputs to linear system, with error checking
    A, M, x, b, postprocess = make_system(A, M, x0, b)

    # Ensure that warnings are always reissued from this function
    import warnings
    warnings.filterwarnings('always', module='pyamg\.krylov\._bicgstab')

    # Check iteration numbers
    if maxiter is None:
        maxiter = len(x) + 5
    elif maxiter < 1:
        raise ValueError('Number of iterations must be positive')

    # Prep for method
    r = b - A*x
    normr = norm(r)

    if residuals is not None:
        residuals[:] = [normr]

    # Check initial guess ( scaling by b, if b != 0,
    #   must account for case when norm(b) is very small)
    normb = norm(b)
    if normb == 0.0:
        normb = 1.0
    if normr < tol*normb:
        return (postprocess(x), 0)

    # Scale tol by ||r_0||_2
    if normr != 0.0:
        tol = tol*normr

    # Is this a one dimensional matrix?
    if A.shape[0] == 1:
        entry = np.ravel(A*np.array([1.0], dtype=xtype))
        return (postprocess(b/entry), 0)

    rstar = r.copy()
    p = r.copy()

    rrstarOld = np.inner(rstar.conjugate(), r)

    iter = 0

    # Begin BiCGStab
    while True:
        Mp = M*p
        AMp = A*Mp

        # alpha = (r_j, rstar) / (A*M*p_j, rstar)
        alpha = rrstarOld/np.inner(rstar.conjugate(), AMp)

        # s_j = r_j - alpha*A*M*p_j
        s = r - alpha*AMp
        Ms = M*s
        AMs = A*Ms

        # omega = (A*M*s_j, s_j)/(A*M*s_j, A*M*s_j)
        omega = np.inner(AMs.conjugate(), s)/np.inner(AMs.conjugate(), AMs)

        # x_{j+1} = x_j +  alpha*M*p_j + omega*M*s_j
        x = x + alpha*Mp + omega*Ms

        # r_{j+1} = s_j - omega*A*M*s
        r = s - omega*AMs

        # beta_j = (r_{j+1}, rstar)/(r_j, rstar) * (alpha/omega)
        rrstarNew = np.inner(rstar.conjugate(), r)
        beta = (rrstarNew / rrstarOld) * (alpha / omega)
        rrstarOld = rrstarNew

        # p_{j+1} = r_{j+1} + beta*(p_j - omega*A*M*p)
        p = r + beta*(p - omega*AMp)

        iter += 1

        normr = norm(r)

        if residuals is not None:
            residuals.append(normr)

        if callback is not None:
            callback(x)

        if normr < tol:
            return (postprocess(x), 0)

        if iter == maxiter:
            return (postprocess(x), iter)