How to use the astroquery.mast.Observations.get_product_list function in astroquery

all_postcards = []
        for i in range(len(post_cens)):
            name = postcard_fmt.format(post_cens['x'].data[i],
                                       post_cens['y'].data[i])
            all_postcards.append(name)
        self.all_postcards = np.array(all_postcards)
        
        
        if local == False:
        
            postcard_obs = Observations.query_criteria(provenance_name="ELEANOR",
                                                       target_name=self.postcard,
                                                       obs_collection="HLSP")

            if len(postcard_obs) > 0:
                product_list = Observations.get_product_list(postcard_obs)
                self.pointing = check_pointing(self.sector, self.camera, self.chip, self.pm_dir)

                if self.pointing is None:
                    extension = ["pc.fits", "bkg.fits", "pm.txt"]
                else:
                    extension = ["pc.fits", "bkg.fits"]

                results = Observations.download_products(product_list, extension=extension,
                                                         download_dir=self.fn_dir)
                postcard_path = results['Local Path'][0]
                self.postcard_path = '/'.join(e for e in postcard_path.split('/')[:-1])
                self.postcard  = results['Local Path'][1].split('/')[-1]
                self.postcard_bkg = results['Local Path'][0].split('/')[-1]
                self.mast_results = results
                self.cutout    = None  # Attribute for TessCut only
                # only downloaded the pointing model if the search for it above failed, so only

print("Error on using juliet function `get_all_TESS_data`: astroquery.mast not found.")
    obs_table = Observations.query_object(object_name,radius=radius)
    out_dict = {}
    times = {}
    fluxes = {}
    fluxes_errors = {}
    for i in range(len(obs_table['dataURL'])):
        if 's_lc.fits' in obs_table['dataURL'][i]:
            fname = obs_table['dataURL'][i].split('/')[-1]
            metadata = fname.split('-')
            if len(metadata) == 5:
                # Extract metadata:
                sector = np.int(metadata[1].split('s')[-1])
                ticid = np.int(metadata[2])
                # Download files:
                data_products = Observations.get_product_list(obs_table[i])
                manifest = Observations.download_products(data_products)
                # Read lightcurve file:
                d,h = fits.getdata('mastDownload/TESS/'+fname[:-8]+'/'+fname,header=True)
                t,fs,fserr,f,ferr = d['TIME']+h['BJDREFI'],d['SAP_FLUX'],d['SAP_FLUX_ERR'],\
                                    d['PDCSAP_FLUX'],d['PDCSAP_FLUX_ERR']
                idx_goodpdc = np.where((f != 0.)&(~np.isnan(f)))[0]
                idx_goodsap = np.where((fs != 0.)&(~np.isnan(fs)))[0]
                # Save to output dictionary:
                if 'TIC' not in out_dict.keys():
                    out_dict['TIC'] = ticid
                out_dict[sector] = {}
                out_dict[sector]['TIME_PDCSAP_FLUX'] = t[idx_goodpdc]
                out_dict[sector]['PDCSAP_FLUX'] = f[idx_goodpdc]
                out_dict[sector]['PDCSAP_FLUX_ERR'] = ferr[idx_goodpdc]
                out_dict[sector]['TIME_SAP_FLUX'] = t[idx_goodsap]
                out_dict[sector]['SAP_FLUX'] = fs[idx_goodsap]

all_errors.append(lcerrors)
			all_flags.append(lcflags)
			sectors.append(sector)

			if delete_fits == 'y':
				os.system('rm -rf '+moonpydir+'/TESS_lcs/'+lcdownload_name)


	except:
		traceback.print_exc()
		time.sleep(60)

		obsTable = Observations.query_object(targID, radius='0.001 deg')
		TESS_idxs = np.where(np.array(obsTable['obs_collection']) == 'TESS')[0]
		minTESSidx, maxTESSidx = np.nanmin(TESS_idxs), np.nanmax(TESS_idxs)+1
		dataproducts = Observations.get_product_list(obsTable[minTESSidx:maxTESSidx])
		timeseries_idxs = np.where(np.array(dataproducts['dataproduct_type']) == 'timeseries')[0]
		obsids = np.array(dataproducts)['obsID'][timeseries_idxs]

		for obsid in np.unique(obsids):
			print ("obsid = ", obsid)
			dataproductsbyID = Observations.get_product_list(obsid)
			manifest = Observations.download_products(dataproductsbyID, download_dir=moonpydir+'/TESS_lcs', dataproduct_type='timeseries', extension='lc.fits', mrp_only=True)
			

			for nmanfile,manfile in enumerate(manifest):
				manfilepath = manfile[0]
				if "_lc.fits" in manfilepath:
					print('found the light curve!')
					### this is the only one you want to save!
					lcpath = manfilepath
					print("lcpath = ", lcpath)

except:
		traceback.print_exc()
		time.sleep(60)

		obsTable = Observations.query_object(targID, radius='0.001 deg')
		TESS_idxs = np.where(np.array(obsTable['obs_collection']) == 'TESS')[0]
		minTESSidx, maxTESSidx = np.nanmin(TESS_idxs), np.nanmax(TESS_idxs)+1
		dataproducts = Observations.get_product_list(obsTable[minTESSidx:maxTESSidx])
		timeseries_idxs = np.where(np.array(dataproducts['dataproduct_type']) == 'timeseries')[0]
		obsids = np.array(dataproducts)['obsID'][timeseries_idxs]

		for obsid in np.unique(obsids):
			print ("obsid = ", obsid)
			dataproductsbyID = Observations.get_product_list(obsid)
			manifest = Observations.download_products(dataproductsbyID, download_dir=moonpydir+'/TESS_lcs', dataproduct_type='timeseries', extension='lc.fits', mrp_only=True)
			

			for nmanfile,manfile in enumerate(manifest):
				manfilepath = manfile[0]
				if "_lc.fits" in manfilepath:
					print('found the light curve!')
					### this is the only one you want to save!
					lcpath = manfilepath
					print("lcpath = ", lcpath)

					### open the file, grab the data!
					lcfile = pyfits.open(lcpath)
					lcfiles.append(lcfile)
					lcdata = lcfile[1].data
					lctimes = np.array(lcdata['TIME'])

tasoc_flux_bkg : np.array
        tasoc_pixel_quality : np.array
             Quality flags for the data; use these not `tasoc_quality`.
        tasoc_pos_corr1 : np.array
        tasoc_pos_corr2 : np.array
        tasoc_mom_centr1 : np.array
        tasoc_mom_centr2 : np.array
        """
        products = Observations.query_object(objectname="TIC"+str(self.tic))

        column = np.where( (products['provenance_name'] == 'TASOC') &
                           (products['target_name'] == str(self.tic)) & 
                           (products['sequence_number'] == self.sector) )[0]

        if len(column) > 0:
            download = Observations.get_product_list(products[column])
            manifest = Observations.download_products(download, download_dir=self.download_dir)
            self.tasoc_path = manifest["Local Path"].data[0]

            hdu = fits.open(self.tasoc_path)

            self.tasoc_header   = hdu[0].header
            self.tasoc_tpf      = hdu[2].data
            self.tasoc_aperture = hdu[3].data
            self.tasoc_time      = hdu[1].data['TIME']
            self.tasoc_quality   = hdu[1].data['QUALITY']
            self.tasoc_timecorr  = hdu[1].data['TIMECORR']
            self.tasoc_cadenceno = hdu[1].data['CADENCENO']
            self.tasoc_flux_raw  = hdu[1].data['FLUX_RAW']
            self.tasoc_flux_bkg  = hdu[1].data['FLUX_BKG']
            self.tasoc_flux_corr = hdu[1].data['FLUX_CORR']
            self.tasoc_pos_corr1 = hdu[1].data['POS_CORR1']