def METHOD_NAME(combined_table: Table, individual_tables: List[Table]) -> Table:
    # Assign sources and licenses of the variables of each individual table to the variables in the combined table.
    combined_table = combined_table.copy()
    for column in combined_table.columns:
        # Initialize sources and licenses for the current variable.
        sources = []
        licenses = []
        for table in individual_tables:
            if column in table.columns:
                # If the current variable was in this table, assign its sources and licenses to the current variable.
                for source in table.metadata.dataset.sources:
                    if source.name not in [known_source.name for known_source in sources]:
                        sources.append(source)
                for license in table.metadata.dataset.licenses:
                    if license.name not in [known_license.name for known_license in licenses]:
                        licenses.append(license)
        # Given that FAOSTAT is the main source of data, place this source first in the list of sources.
        sources_without_faostat = [source for source in sources if source.name != FAOSTAT_SOURCE_NAME]
        # Sort the rest of the sources in alphabetical order.
        sources_sorting = np.argsort([source.name for source in sources_without_faostat])
        sources = [source for source in sources if source.name == FAOSTAT_SOURCE_NAME] + np.array(
            sources_without_faostat
        )[sources_sorting].tolist()
        combined_table[column].metadata.sources = sources
        combined_table[column].metadata.licenses = licenses
        # Generate a title for this variable using the column name.
        title = column.capitalize().replace("_", " ").replace("  ", " ").replace("n e c", "n.e.c.")
        combined_table[column].metadata.title = title
        # Define units.
        combined_table[column].metadata.unit = "tonnes per hectare"
        combined_table[column].metadata.short_unit = "t/ha"
    return combined_table