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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 pixel_kernel(point_source_kernel, subgrid_res=7):
 """ converts a pixelised kernel of a point source to a kernel representing a uniform extended pixel :param point_source_kernel: :param subgrid_res: :return: convolution kernel for an extended pixel """

    kernel_subgrid = subgrid_kernel(point_source_kernel, subgrid_res, num_iter=10)
    kernel_size = len(point_source_kernel)
    kernel_pixel = np.zeros((kernel_size*subgrid_res, kernel_size*subgrid_res))
    for i in range(subgrid_res):
        k_x = int((kernel_size-1) / 2 * subgrid_res + i)
        for j in range(subgrid_res):
            k_y = int((kernel_size-1) / 2 * subgrid_res + j)
            kernel_pixel = image_util.add_layer2image(kernel_pixel, k_x, k_y, kernel_subgrid)
    kernel_pixel = util.averaging(kernel_pixel, numGrid=kernel_size*subgrid_res, numPix=kernel_size)
    return kernel_norm(kernel_pixel)