def METHOD_NAME(nyc_region):
    dc_state_region = pipeline.Region.from_fips("11")
    dc_county_region = pipeline.Region.from_fips("11001")
    dataset = combined_datasets.load_us_timeseries_dataset(
        load_demographics=False
    ).get_regions_subset([nyc_region, dc_state_region, dc_county_region])
    # TODO(tom): Find some way to have this test read data that hasn't already gone
    # through replace_dc_county_with_state_data and remove this hack.
    timeseries_modified = dataset.timeseries.copy()
    dc_state_rows = (
        timeseries_modified.index.get_level_values("location_id") == dc_state_region.location_id
    )
    # Modify DC state cases so that they're not equal to county values.
    timeseries_modified.loc[dc_state_rows, CommonFields.CASES] = 10
    dataset = dataclasses.replace(dataset, timeseries=timeseries_modified, timeseries_bucketed=None)
    # Verify that county and state DC numbers are different to better
    # assert after that the replace worked.
    dc_state_dataset = dataset.get_regions_subset([dc_state_region])
    dc_county_dataset = dataset.get_regions_subset([dc_county_region])
    ts1 = dc_state_dataset.timeseries[CommonFields.CASES].reset_index(drop=True)
    ts2 = dc_county_dataset.timeseries[CommonFields.CASES].reset_index(drop=True)
    assert not ts1.equals(ts2)
    output = custom_aggregations.replace_dc_county_with_state_data(dataset)
    # Verify that the regions are the same input and output
    assert set(dataset.location_ids) == set(output.location_ids)
    # Verify that the state cases from before replacement are now the
    # same as the county cases
    dc_county_dataset = output.get_regions_subset([dc_county_region])
    ts1 = dc_state_dataset.timeseries[CommonFields.CASES].reset_index(drop=True)
    ts2 = dc_county_dataset.timeseries[CommonFields.CASES].reset_index(drop=True)
    assert ts1.equals(ts2)