def METHOD_NAME(self, mock_credentials, mock_read_gbq):
    # Set up fake data.
    state_data = pd.read_csv(
        good_input["state"], parse_dates=["date"])[keep_cols]
    county_data = pd.read_csv(
        good_input["county"], parse_dates=["date"])[keep_cols]
    # Mocks
    mock_read_gbq.side_effect = [state_data, county_data]
    mock_credentials.return_value = None
    dfs = pull_gs_data("", datetime.strptime(
        "20201230", "%Y%m%d"), datetime.combine(date.today(), datetime.min.time()), 0)
    for level in ["county", "state"]:
        df = dfs[level]
        assert (
            df.columns.values
            == ["geo_id", "timestamp"] + METRICS + COMBINED_METRIC
        ).all()
        # combined_symptoms is nan when both Anosmia, Ageusia, and Dysgeusia are nan
        assert sum(~df.loc[
            (df[METRICS[23]].isnull())
            & (df[METRICS[24]].isnull())
            & (df[METRICS[25]].isnull()), COMBINED_METRIC[4]].isnull()) == 0
        # combined_symptoms is not nan when at least one of them isn't nan
        assert sum(df.loc[
            (~df[METRICS[23]].isnull())
            & (df[METRICS[24]].isnull())
            & (df[METRICS[25]].isnull()), COMBINED_METRIC[4]].isnull()) == 0
        assert sum(df.loc[
            (df[METRICS[23]].isnull())
            & (~df[METRICS[24]].isnull())
            & (df[METRICS[25]].isnull()), COMBINED_METRIC[4]].isnull()) == 0
        assert sum(df.loc[
            (df[METRICS[23]].isnull())
            & (df[METRICS[24]].isnull())
            & (~df[METRICS[25]].isnull()), COMBINED_METRIC[4]].isnull()) == 0
        assert sum(df.loc[
            (~df[METRICS[23]].isnull())
            & (~df[METRICS[24]].isnull())
            & (df[METRICS[25]].isnull()), COMBINED_METRIC[4]].isnull()) == 0
        assert sum(df.loc[
            (~df[METRICS[23]].isnull())
            & (df[METRICS[24]].isnull())
            & (~df[METRICS[25]].isnull()), COMBINED_METRIC[4]].isnull()) == 0
        assert sum(df.loc[
            (df[METRICS[23]].isnull())
            & (~df[METRICS[24]].isnull())
            & (~df[METRICS[25]].isnull()), COMBINED_METRIC[4]].isnull()) == 0