def METHOD_NAME(
    dataset_in: timeseries.MultiRegionDataset,
) -> timeseries.MultiRegionDataset:
    """Replace DC County data with data from State.
    Args:
        dataset_in: Input dataset.
    Returns: Dataset with DC county data replaced to match DC state.
    """
    dc_state_region = pipeline.Region.from_fips(DC_STATE_FIPS)
    dc_county_region = pipeline.Region.from_fips(DC_COUNTY_FIPS)
    dc_map = {dc_state_region: dc_county_region}
    # aggregate_regions only copies number columns. Extract them and re-add to the aggregated
    # dataset.
    dataset_with_dc_county, dataset_without_dc_county = dataset_in.partition_by_region(
        [dc_county_region]
    )
    static_excluding_numbers = dataset_with_dc_county.static.select_dtypes(exclude="number")
    dc_county_dataset = region_aggregation.aggregate_regions(dataset_in, dc_map).add_static_values(
        static_excluding_numbers.reset_index()
    )
    # The aggregation will have replaced hsa_population with nan.  We need to fix it.
    dc_county_dataset.static.loc[
        :, CommonFields.HSA_POPULATION
    ] = dataset_with_dc_county.static.loc[:, CommonFields.HSA_POPULATION]
    return dataset_without_dc_county.append_regions(dc_county_dataset)