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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 findbeam_slices(data, orig_initial, mask=None, maxiter=0, epsfcn=0.001, dmin=0, dmax=np.inf, sector_width=np.pi / 9.0, extent=10, callback=None):
 """Find beam center with the "slices" method Inputs: data: scattering matrix orig_initial: estimated value for x (row) and y (column) coordinates of the beam center, starting from 1. mask: mask matrix. If None, nothing will be masked. Otherwise it should be of the same size as data. Nonzero means non-masked. maxiter: maximum number of iterations for scipy.optimize.leastsq epsfcn: input for scipy.optimize.leastsq dmin: disregard pixels nearer to the origin than this dmax: disregard pixels farther from the origin than this sector_width: width of sectors in radians extent: approximate distance of the current and the real origin in pixels. Too high a value makes the fitting procedure unstable. Too low a value does not permit to move away the current origin. callback: callback function (expects no arguments) Output: a vector of length 2 with the x (row) and y (column) coordinates of the origin. """

    if mask is None:
        mask = np.ones(data.shape)
    data = data.astype(np.double)

    def targetfunc(orig, data, mask, orig_orig, callback):
        # integrate four sectors
        I = [None] * 4
        p, Ints, A = radint_nsector(data, None, -1, -1, -1, orig[0] + orig_orig[0], orig[1] + orig_orig[1], mask=mask,
                                    phi0=np.pi / 4 - 0.5 * sector_width, dphi=sector_width,
                                    Nsector=4)
        minpix = max(max(p.min(0).tolist()), dmin)
        maxpix = min(min(p.max(0).tolist()), dmax)
        if (maxpix < minpix):
            raise ValueError('The four slices do not overlap! Please give a\
 better approximation for the origin or use another centering method.')
        for i in range(4):
            I[i] = Ints[:, i][(p[:, i] >= minpix) & (p[:, i] <= maxpix)]
        ret = ((I[0] - I[2]) ** 2 + (I[1] - I[3]) ** 2) / (maxpix - minpix)
        if callback is not None:
            callback()
        return ret
    orig = scipy.optimize.leastsq(targetfunc, np.array([extent, extent]),
                                  args=(data, 1 - mask.astype(np.uint8),
                                        np.array(orig_initial) - extent, callback),
                                  maxfev=maxiter, epsfcn=0.01)
    return orig[0] + np.array(orig_initial) - extent