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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 data_configure_simple(numPix, deltaPix, exposure_time=1, sigma_bkg=1, inverse=False):
 """ configures the data keyword arguments with a coordinate grid centered at zero. :param numPix: number of pixel (numPix x numPix) :param deltaPix: pixel size (in angular units) :param exposure_time: exposure time :param sigma_bkg: background noise (Gaussian sigma) :param inverse: if True, coordinate system is ra to the left, if False, to the right :return: keyword arguments that can be used to construct a Data() class instance of lenstronomy """

    mean = 0.  # background mean flux (default zero)
    # 1d list of coordinates (x,y) of a numPix x numPix square grid, centered to zero
    x_grid, y_grid, ra_at_xy_0, dec_at_xy_0, x_at_radec_0, y_at_radec_0, Mpix2coord, Mcoord2pix = util.make_grid_with_coordtransform(numPix=numPix, deltapix=deltaPix, subgrid_res=1, inverse=inverse)
    # mask (1= model this pixel, 0= leave blanck)
    exposure_map = np.ones((numPix, numPix)) * exposure_time  # individual exposure time/weight per pixel

    kwargs_data = {
        'background_rms': sigma_bkg,
        'exposure_map': exposure_map
        , 'ra_at_xy_0': ra_at_xy_0, 'dec_at_xy_0': dec_at_xy_0, 'transform_pix2angle': Mpix2coord
        , 'image_data': np.zeros((numPix, numPix))
        }
    return kwargs_data