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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 split_kernel(kernel, kernel_subgrid, subsampling_size, subgrid_res):
 """ pixel kernel and subsampling kernel such that the convolution of both applied on an image can be performed, i.e. smaller subsampling PSF and hole in larger PSF :param kernel: PSF kernel of the size of the pixel :param kernel_subgrid: subsampled kernel :param subsampling_size: size of subsampling PSF in units of image pixels :return: pixel and subsampling kernel """

    n = len(kernel)
    n_sub = len(kernel_subgrid)
    if subsampling_size % 2 == 0:
        subsampling_size += 1
    if subsampling_size > n:
        subsampling_size = n

    kernel_hole = copy.deepcopy(kernel)
    n_min = int((n-1)/2 - (subsampling_size-1)/2)
    n_max = int((n-1)/2 + (subsampling_size-1)/2 + 1)
    kernel_hole[n_min:n_max, n_min:n_max] = 0
    n_min_sub = int((n_sub - 1) / 2 - (subsampling_size*subgrid_res - 1) / 2)
    n_max_sub = int((n_sub - 1) / 2 + (subsampling_size * subgrid_res - 1) / 2 + 1)
    kernel_subgrid_cut = kernel_subgrid[n_min_sub:n_max_sub, n_min_sub:n_max_sub]
    flux_subsampled = np.sum(kernel_subgrid_cut)
    flux_hole = np.sum(kernel_hole)
    if flux_hole > 0:
        kernel_hole *= (1. - flux_subsampled) / np.sum(kernel_hole)
    else:
        kernel_subgrid_cut /= np.sum(kernel_subgrid_cut)
    return kernel_hole, kernel_subgrid_cut