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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 _cont_norm(fluxes, ivars, cont):
 """ Continuum-normalize a continuous segment of spectra. Parameters fluxes: numpy ndarray pixel intensities ivars: numpy ndarray inverse variances, parallel to fluxes contmask: boolean mask True indicates that pixel is continuum Returns ------- norm_fluxes: numpy ndarray normalized pixel intensities norm_ivars: numpy ndarray rescaled inverse variances """

    nstars = fluxes.shape[0]
    npixels = fluxes.shape[1]
    norm_fluxes = np.ones(fluxes.shape)
    norm_ivars = np.zeros(ivars.shape)
    bad = cont == 0.
    norm_fluxes = np.ones(fluxes.shape)
    norm_fluxes[~bad] = fluxes[~bad] / cont[~bad]
    norm_ivars = cont**2 * ivars
    return norm_fluxes, norm_ivars