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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 solvedbi_sm(ah, rho, b, c=None, axis=4):
 r""" Solve a diagonal block linear system with a scaled identity term using the Sherman-Morrison equation. The solution is obtained by independently solving a set of linear systems of the form (see :cite:`wohlberg-2016-efficient`) .. math:: (\rho I + \mathbf{a} \mathbf{a}^H ) \; \mathbf{x} = \mathbf{b} \;\;. In this equation inner products and matrix products are taken along the specified axis of the corresponding multi-dimensional arrays; the solutions are independent over the other axes. Parameters ah : array_like Linear system component :math:`\mathbf{a}^H` rho : float Linear system parameter :math:`\rho` b : array_like Linear system component :math:`\mathbf{b}` c : array_like, optional (default None) Solution component :math:`\mathbf{c}` that may be pre-computed using :func:`solvedbi_sm_c` and cached for re-use. axis : int, optional (default 4) Axis along which to solve the linear system Returns ------- x : ndarray Linear system solution :math:`\mathbf{x}` """


    a = np.conj(ah)
    if c is None:
        c = solvedbi_sm_c(ah, a, rho, axis)
    if have_numexpr:
        cb = inner(c, b, axis=axis)
        return ne.evaluate('(b - (a * cb)) / rho')
    else:
        return (b - (a * inner(c, b, axis=axis))) / rho