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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 centroid_sources(data, xpos, ypos, box_size=11, footprint=None, error=None, mask=None, centroid_func=centroid_com):
 """ Calculate the centroid of sources at the defined positions. A cutout image centered on each input position will be used to calculate the centroid position. The cutout image is defined either using the ``box_size`` or ``footprint`` keyword. The ``footprint`` keyword can be used to create a non-rectangular cutout image. Parameters data : array_like The 2D array of the image. xpos, ypos : float or array-like of float The initial ``x`` and ``y`` pixel position(s) of the center position. A cutout image centered on this position be used to calculate the centroid. box_size : int or array-like of int, optional The size of the cutout image along each axis. If ``box_size`` is a number, then a square cutout of ``box_size`` will be created. If ``box_size`` has two elements, they should be in ``(ny, nx)`` order. footprint : `~numpy.ndarray` of bools, optional A 2D boolean array where `True` values describe the local footprint region to cutout. ``footprint`` can be used to create a non-rectangular cutout image, in which case the input ``xpos`` and ``ypos`` represent the center of the minimal bounding box for the input ``footprint``. ``box_size=(n, m)`` is equivalent to ``footprint=np.ones((n, m))``. Either ``box_size`` or ``footprint`` must be defined. If they are both defined, then ``footprint`` overrides ``box_size``. mask : array_like, bool, optional A 2D boolean array with the same shape as ``data``, where a `True` value indicates the corresponding element of ``data`` is masked. error : array_like, optional The 2D array of the 1-sigma errors of the input ``data``. ``error`` must have the same shape as ``data``. ``error`` will be used only if supported by the input ``centroid_func``. centroid_func : callable, optional A callable object (e.g. function or class) that is used to calculate the centroid of a 2D array. The ``centroid_func`` must accept a 2D `~numpy.ndarray`, have a ``mask`` keyword and optionally an ``error`` keyword. The callable object must return a tuple of two 1D `~numpy.ndarray`\\s, representing the x and y centroids. The default is `~photutils.centroids.centroid_com`. Returns ------- xcentroid, ycentroid : `~numpy.ndarray` The ``x`` and ``y`` pixel position(s) of the centroids. """


    xpos = np.atleast_1d(xpos)
    ypos = np.atleast_1d(ypos)
    if xpos.ndim != 1:
        raise ValueError('xpos must be a 1D array.')
    if ypos.ndim != 1:
        raise ValueError('ypos must be a 1D array.')

    if footprint is None:
        if box_size is None:
            raise ValueError('box_size or footprint must be defined.')
        else:
            box_size = np.atleast_1d(box_size)
            if len(box_size) == 1:
                box_size = np.repeat(box_size, 2)
            if len(box_size) != 2:
                raise ValueError('box_size must have 1 or 2 elements.')

        footprint = np.ones(box_size, dtype=bool)
    else:
        footprint = np.asanyarray(footprint, dtype=bool)
        if footprint.ndim != 2:
            raise ValueError('footprint must be a 2D array.')

    use_error = False
    spec = inspect.getfullargspec(centroid_func)
    if 'mask' not in spec.args:
        raise ValueError('The input "centroid_func" must have a "mask" '
                         'keyword.')
    if 'error' in spec.args:
        use_error = True

    xcentroids = []
    ycentroids = []
    for xp, yp in zip(xpos, ypos):
        slices_large, slices_small = overlap_slices(data.shape,
                                                    footprint.shape, (yp, xp))
        data_cutout = data[slices_large]

        mask_cutout = None
        if mask is not None:
            mask_cutout = mask[slices_large]

        footprint_mask = ~footprint
        # trim footprint mask if partial overlap on the data
        footprint_mask = footprint_mask[slices_small]

        if mask_cutout is None:
            mask_cutout = footprint_mask
        else:
            # combine the input mask and footprint mask
            mask_cutout = np.logical_or(mask_cutout, footprint_mask)

        if error is not None and use_error:
            error_cutout = error[slices_large]
            xcen, ycen = centroid_func(data_cutout, mask=mask_cutout,
                                       error=error_cutout)
        else:
            xcen, ycen = centroid_func(data_cutout, mask=mask_cutout)

        xcentroids.append(xcen + slices_large[1].start)
        ycentroids.append(ycen + slices_large[0].start)

    return np.array(xcentroids), np.array(ycentroids)