How to use the pybliometrics.scopus.utils.cache_file function in pybliometrics

Note: These information will not be cached and are slow for large
        coauthor groups.
        """
        # Get number of authors to search for
        res = cache_file(url=self.coauthor_link)
        data = loads(res.text)['search-results']
        N = int(data.get('opensearch:totalResults', 0))
        # Store information in namedtuples
        fields = 'surname given_name id areas affiliation_id name city country'
        coauth = namedtuple('Coauthor', fields)
        coauthors = []
        # Iterate over search results in chunks of 25 results
        count = 0
        while count < N:
            params = {'start': count, 'count': 25}
            res = cache_file(url=self.coauthor_link, params=params, accept='json')
            data = loads(res.text)['search-results'].get('entry', [])
            # Extract information for each coauthor
            for entry in data:
                aff = entry.get('affiliation-current', {})
                try:
                    areas = [a['$'] for a in entry.get('subject-area', [])]
                except TypeError:  # Only one subject area given
                    areas = [entry['subject-area']['$']]
                new = coauth(surname=entry['preferred-name']['surname'],
                    given_name=entry['preferred-name'].get('given-name'),
                    id=entry['dc:identifier'].split(':')[-1],
                    areas='; '.join(areas), name=aff.get('affiliation-name'),
                    affiliation_id=aff.get('affiliation-id'),
                    city=aff.get('affiliation-city'),
                    country=aff.get('affiliation-country'))
                coauthors.append(new)

_json = res.get('search-results', {}).get('entry', [])
    if verbose:
        chunk = 1
        chunks = int(n/params['count']) + (n % params['count'] > 0) + 1 #roundup + 1 for the final iteration
        print('Downloading results for query "{}":'.format(params['query']))
        print_progress(chunk, chunks)
    # Download the remaining information in chunks
    while n > 0:
        n -= params["count"]
        if cursor:
            pointer = res['search-results']['cursor'].get('@next')
            params.update({'cursor': pointer})
        else:
            start += params["count"]
            params.update({'start': start})
        res = cache_file(url=SEARCH_URL[api], params=params, **kwds).json()
        _json.extend(res.get('search-results', {}).get('entry', []))
        if verbose:
            chunk += 1
            print_progress(chunk, chunks)
    return _json

def get_coauthors(self):
        """Retrieves basic information about co-authors as a list of
        namedtuples in the form
        (surname, given_name, id, areas, affiliation_id, name, city, country),
        where areas is a list of subject area codes joined by "; ".
        Note: These information will not be cached and are slow for large
        coauthor groups.
        """
        # Get number of authors to search for
        res = cache_file(url=self.coauthor_link)
        data = loads(res.text)['search-results']
        N = int(data.get('opensearch:totalResults', 0))
        # Store information in namedtuples
        fields = 'surname given_name id areas affiliation_id name city country'
        coauth = namedtuple('Coauthor', fields)
        coauthors = []
        # Iterate over search results in chunks of 25 results
        count = 0
        while count < N:
            params = {'start': count, 'count': 25}
            res = cache_file(url=self.coauthor_link, params=params, accept='json')
            data = loads(res.text)['search-results'].get('entry', [])
            # Extract information for each coauthor
            for entry in data:
                aff = entry.get('affiliation-current', {})
                try: