def METHOD_NAME(linker: Linker, example_rows_per_category=2):
    sqls = []
    uid_cols = linker._settings_obj._unique_id_input_columns
    uid_cols_l = [uid_col.name_l() for uid_col in uid_cols]
    uid_cols_r = [uid_col.name_r() for uid_col in uid_cols]
    uid_cols = uid_cols_l + uid_cols_r
    uid_expr = " || '-' ||".join(uid_cols)
    gamma_columns = [c._gamma_column_name for c in linker._settings_obj.comparisons]
    gam_concat = " || ',' || ".join(gamma_columns)
    sql = f"""
    select
        *,
        {uid_expr} as rec_comparison_id,
        {gam_concat} as gam_concat,
        random() as rand_order
    from __splink__df_predict
    """
    sql = {
        "sql": sql,
        "output_table_name": "__splink__df_predict_with_row_id",
    }
    sqls.append(sql)
    sql = """
    select *,
        ROW_NUMBER() OVER (PARTITION BY gam_concat order by rand_order)
            AS row_example_index,
        COUNT(*) OVER (PARTITION BY gam_concat) AS count
    from __splink__df_predict_with_row_id
    """
    sql = {
        "sql": sql,
        "output_table_name": "__splink__df_predict_with_row_num",
    }
    sqls.append(sql)
    sql = f"""
    select *
    from __splink__df_predict_with_row_num
    where row_example_index <= {example_rows_per_category}
    """
    sql = {
        "sql": sql,
        "output_table_name": "__splink__df_example_rows",
    }
    sqls.append(sql)
    return sqls