def METHOD_NAME(input_file: Path) -> dict:
    # We create a mapping to associate the regions and their sub-regions
    regions_mapping = defaultdict(set)
    with open(input_file, 'rt', encoding='utf-8') as csvfile:
        csv_reader = csv.reader(csvfile, delimiter=',')
        next(csv_reader)
        for row in csv_reader:
            '''
            gc: Global Code
            gn: Global Name
            rc: Region Code
            rn: Region Name
            src: Sub-region Code
            srn: Sub-region Name
            irc: Intermediate Region Code
            irn: Intermediate Region Name
            mc: M49 Code
            coa: Country of Area
            '''
            gc, gn, rc, rn, src, srn, irc, irn, mc, coa, *_ = row
            global_region = f'{gc} - {gn}'
            if rc and rn:
                # Almost all the areas have a region information
                region = f'{rc} - {rn}'
                regions_mapping[global_region].add(region)
                # Deal with the region information
                sub_region = f'{src} - {srn}'
                regions_mapping[region].add(sub_region)
                country = f'{mc} - {coa}'
                if irc and irn:
                    # If the country is located in an intermediate region
                    inter_region = f'{irc} - {irn}'
                    # Deal with the sub-region information
                    regions_mapping[sub_region].add(inter_region)
                    # Deal with the intermediate region information
                    regions_mapping[inter_region].add(country)
                else:
                    # The country is located in a sub-region
                    regions_mapping[sub_region].add(country)
            else:
                # Should be only Antarctica which has only global region and
                # country information
                country = f'{mc} - {coa}'
                regions_mapping[global_region].add(country)
                regions_mapping[country] = None
    return regions_mapping