How to use the feast.sdk.utils.gs_utils.is_gs_path function in feast

def _get_remote_location(path, staging_location):
    """Get the remote location of the file
    
    Args:
        path (str): raw path of the file
        staging_location (str): path to stage the file

    """
    if is_gs_path(path):
        return path, False

    if staging_location is None:
        return "", True

    if not is_gs_path(staging_location):
        raise ValueError("Staging location must be in GCS")

    filename = ntpath.basename(path)
    return staging_location + "/" + filename, True

def _get_remote_location(path, staging_location):
    """Get the remote location of the file
    
    Args:
        path (str): raw path of the file
        staging_location (str): path to stage the file

    """
    if is_gs_path(path):
        return path, False

    if staging_location is None:
        return "", True

    if not is_gs_path(staging_location):
        raise ValueError("Staging location must be in GCS")

    filename = ntpath.basename(path)
    return staging_location + "/" + filename, True

Returns: (str) path to the downloaded file

        """
        if not staging_location:
            df = self.download_table_as_df(full_table_id)
            if file_type == FileType.CSV:
                df.to_csv(dest, index=False)
            elif file_type == FileType.JSON:
                df.to_json(dest, index=False)
            else:
                raise ValueError(
                    "Only FileType: CSV and JSON are supported for download_table_as_file without staging location"
                )
            return dest

        if not is_gs_path(staging_location):
            raise ValueError("staging_uri must be a directory in GCS")

        shard_folder = self.__extract_table_to_shard_folder(
            full_table_id, staging_location, file_type)
        return gcs_folder_to_file(shard_folder, dest)

Timestamp value to assign to all features in the dataset.
            serving_store (feast.sdk.resources.feature.DataStore): Defaults to None.
                Serving store to write the features in this instance to.
            warehouse_store (feast.sdk.resources.feature.DataStore): Defaults to None.
                Warehouse store to write the features in this instance to.
            job_options (dict): Defaults to empty dict. Additional job options.
        
        Returns:
            Importer: the importer for the dataset provided.
        """
        src_type = "file.csv"
        source_options = {}
        source_options["path"], require_staging = _get_remote_location(
            path, staging_location
        )
        if is_gs_path(path):
            df = gcs_to_df(path)
        else:
            df = pd.read_csv(path)
        schema, features = _detect_schema_and_feature(
            entity,
            owner,
            id_column,
            feature_columns,
            timestamp_column,
            timestamp_value,
            serving_store,
            warehouse_store,
            df,
        )
        iport_spec = _create_import(
            src_type, source_options, job_options, entity, schema

"""
        Download a BigQuery table as Pandas Dataframe
        Args:
            full_table_id (src) : fully qualified BigQuery table id
            staging_location: url to staging_location (currently
                support a folder in GCS)

        Returns: pandas.DataFrame: dataframe of the training dataset

        """
        if not staging_location:
            table = bigquery.TableReference.from_string(full_table_id)
            rows = self.bqclient.list_rows(table)
            return rows.to_dataframe(bqstorage_client=self.bqstorageclient)

        if not is_gs_path(staging_location):
            raise ValueError("staging_uri must be a directory in GCS")

        shard_folder = self.__extract_table_to_shard_folder(
            full_table_id, staging_location, DestinationFormat.CSV)
        return gcs_folder_to_df(shard_folder)