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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 _find_cont_running_quantile(wl, fluxes, ivars, q, delta_lambda, verbose=False):
 """ Perform continuum normalization using a running quantile Parameters wl: numpy ndarray wavelength vector fluxes: numpy ndarray of shape (nstars, npixels) pixel intensities ivars: numpy ndarray of shape (nstars, npixels) inverse variances, parallel to fluxes q: float the desired quantile cut delta_lambda: int the number of pixels over which the median is calculated Output ------ norm_fluxes: numpy ndarray of shape (nstars, npixels) normalized pixel intensities norm_ivars: numpy ndarray of shape (nstars, npixels) rescaled pixel invariances """

    cont = np.zeros(fluxes.shape)
    nstars = fluxes.shape[0]
    for jj in range(nstars):
        if verbose:
            print("cont_norm_q(): working on star [%s/%s]..." % (jj+1, nstars))
        flux = fluxes[jj,:]
        ivar = ivars[jj,:]
        for ll, lam in enumerate(wl):
            indx = (np.where(abs(wl-lam) < delta_lambda))[0]
            flux_cut = flux[indx]
            ivar_cut = ivar[indx]
            cont[jj, ll] = _weighted_median(flux_cut, ivar_cut, q)
    return cont