How to use the gseapy.utils.DEFAULT_CACHE_PATH function in gseapy

def _download_libraries(self, libname):
        """ download enrichr libraries."""
        self._logger.info("Downloading and generating Enrichr library gene sets......")
        s = retry(5)
        # queery string
        ENRICHR_URL = 'http://amp.pharm.mssm.edu/Enrichr/geneSetLibrary'
        query_string = '?mode=text&libraryName=%s'
        # get
        response = s.get( ENRICHR_URL + query_string % libname, timeout=None)
        if not response.ok:
            raise Exception('Error fetching enrichment results, check internet connection first.')
        # reformat to dict and save to disk
        mkdirs(DEFAULT_CACHE_PATH)
        genesets_dict = {}
        outname = "enrichr.%s.gmt"%libname
        gmtout = open(os.path.join(DEFAULT_CACHE_PATH, outname), "w")
        for line in response.iter_lines(chunk_size=1024, decode_unicode='utf-8'):
            line=line.strip()
            k = line.split("\t")[0]
            v = list(map(lambda x: x.split(",")[0], line.split("\t")[2:]))
            genesets_dict.update({ k: v})
            outline = "%s\t\t%s\n"%(k, "\t".join(v))
            gmtout.write(outline)
        gmtout.close()

        return genesets_dict

def _download_libraries(self, libname):
        """ download enrichr libraries."""
        self._logger.info("Downloading and generating Enrichr library gene sets......")
        s = retry(5)
        # queery string
        ENRICHR_URL = 'http://amp.pharm.mssm.edu/Enrichr/geneSetLibrary'
        query_string = '?mode=text&libraryName=%s'
        # get
        response = s.get( ENRICHR_URL + query_string % libname, timeout=None)
        if not response.ok:
            raise Exception('Error fetching enrichment results, check internet connection first.')
        # reformat to dict and save to disk
        mkdirs(DEFAULT_CACHE_PATH)
        genesets_dict = {}
        outname = "enrichr.%s.gmt"%libname
        gmtout = open(os.path.join(DEFAULT_CACHE_PATH, outname), "w")
        for line in response.iter_lines(chunk_size=1024, decode_unicode='utf-8'):
            line=line.strip()
            k = line.split("\t")[0]
            v = list(map(lambda x: x.split(",")[0], line.split("\t")[2:]))
            genesets_dict.update({ k: v})
            outline = "%s\t\t%s\n"%(k, "\t".join(v))
            gmtout.write(outline)
        gmtout.close()

        return genesets_dict

if gmt.lower().endswith(".gmt"):
            with open(gmt) as genesets:
                 genesets_dict = { line.strip().split("\t")[0]: line.strip().split("\t")[2:]
                                  for line in genesets.readlines()}
            return genesets_dict

        elif gmt in DEFAULT_LIBRARY:
            pass
        elif gmt in self.get_libraries():
            pass
        else:
            self._logger.error("No supported gene_sets: %s"%gmt)
            sys.exit(0)

        tmpname = "enrichr." + gmt + ".gmt"
        tempath = os.path.join(DEFAULT_CACHE_PATH, tmpname)
        # if file already download
        if os.path.isfile(tempath):
            self._logger.info("Enrichr library gene sets already downloaded in: %s, use local file"%DEFAULT_CACHE_PATH)
            return self.parse_gmt(tempath)
        else:
            return self._download_libraries(gmt)

self.add_dataset_to_xml(dataset)
        for at in attributes:
            self.add_attribute_to_xml(at)
        # add filters
        if filters:
            for k, v in filters.items(): 
                if isinstance(v, list): v = ",".join(v)
                self.add_filter_to_xml(k, v)

        xml_query = self.get_xml()
        results = super(Biomart, self).query(xml_query)
        df = pd.read_csv(StringIO(results), header=None, sep="\t",
                         names=attributes, index_col=None)
        # save file to cache path.
        if filename is None: 
            mkdirs(DEFAULT_CACHE_PATH)
            filename = os.path.join(DEFAULT_CACHE_PATH, "{}.background.genes.txt".format(dataset))
        df.to_csv(filename, sep="\t", index=False)
      
        return df

for at in attributes:
            self.add_attribute_to_xml(at)
        # add filters
        if filters:
            for k, v in filters.items(): 
                if isinstance(v, list): v = ",".join(v)
                self.add_filter_to_xml(k, v)

        xml_query = self.get_xml()
        results = super(Biomart, self).query(xml_query)
        df = pd.read_csv(StringIO(results), header=None, sep="\t",
                         names=attributes, index_col=None)
        # save file to cache path.
        if filename is None: 
            mkdirs(DEFAULT_CACHE_PATH)
            filename = os.path.join(DEFAULT_CACHE_PATH, "{}.background.genes.txt".format(dataset))
        df.to_csv(filename, sep="\t", index=False)
      
        return df