How to use the cliboa.util.cache.ObjectStore.put function in cliboa

self._timeout,
            self._retry_count,
            self._port,
        )
        files = sftp.list_files(
            self._src_dir, self._dest_dir, re.compile(self._src_pattern)
        )

        if self._quit is True and len(files) == 0:
            self._logger.info("No file was found. After process will not be processed")
            return StepStatus.SUCCESSFUL_TERMINATION

        self._logger.info("Files downloaded %s" % files)

        # cache downloaded file names
        ObjectStore.put(self._step, files)

self._password,
            self._timeout,
            self._retry_count,
            self._port,
            self._tls,
        )
        files = ftp_util.list_files(
            self._src_dir, self._dest_dir, re.compile(self._src_pattern)
        )

        if self._quit is True and len(files) == 0:
            self._logger.info("No file was found. After process will not be processed")
            return StepStatus.SUCCESSFUL_TERMINATION

        # cache downloaded file names
        ObjectStore.put(self._step, files)

def _save_to_cache(self):
        self._logger.info("Save data to on memory")
        df = pandas.read_gbq(
            query="SELECT * FROM %s.%s" % (self._dataset, self._tblname)
            if self._query is None
            else self._query,
            dialect="standard",
            location=self._location,
            project_id=self._project_id,
            credentials=ServiceAccount.auth(self._credentials),
        )
        ObjectStore.put(self._key, df)

valid = EssentialParameters(self.__class__.__name__, [self._src_pattern])
        valid()

        client = Gcs.get_gcs_client(self._credentials)
        bucket = client.get_bucket(self._bucket)
        dl_files = []
        for blob in bucket.list_blobs(prefix=self._prefix, delimiter=self._delimiter):
            r = re.compile(self._src_pattern)
            if not r.fullmatch(blob.name):
                continue
            dl_files.append(blob.name)
            blob.download_to_filename(
                os.path.join(self._dest_dir, os.path.basename(blob.name))
            )

        ObjectStore.put(self._step, dl_files)

# extract only the specified columns
                    row_dict = {}
                    for c in self._columns:
                        if not row.get(c):
                            continue
                        row_dict[c] = row.get(c)
                    self._s.save(row_dict)
            else:
                reader = csv.reader(f)
                header = next(reader, None)
                for row in reader:
                    row_dict = dict(zip(header, row))
                    self._s.save(row_dict)

        # cache downloaded file names
        ObjectStore.put(self._step, files)

def execute(self, *args):
        super().execute()
        valid = EssentialParameters(self.__class__.__name__, [self._key])
        valid()

        df = pandas.read_gbq(
            query=self._get_query(),
            dialect="standard",
            location=self._location,
            project_id=self._project_id,
            credentials=ServiceAccount.auth(self._credentials),
        )
        ObjectStore.put(self._key, df)