code: def _get_answer_coordinates(table, sql_query):
      """Retrieves references coordinates by executing SQL."""
      # MAX and MIN are automatically supported by the model.
      aggregation_op_index = sql_query["agg"]
      if aggregation_op_index >= 3:
          aggregation_op = _Aggregation(aggregation_op_index)
      else:
          aggregation_op = _Aggregation.NONE
  
      target_column = sql_query["sel"]
      conditions = [
          _Condition(column, _Operator(operator), cmp_value)
          for column, operator, cmp_value in zip(
              sql_query["conds"]["column_index"],
              sql_query["conds"]["operator_index"],
              sql_query["conds"]["condition"],
          )
      ]
  
      indices = []
      for row in range(len(table["rows"])):
          if _respect_conditions(table, table["rows"][row], conditions):
              indices.append((row, target_column))
  
      if not indices:
          return [], aggregation_op
  
      if len(indices) == 1:
          return indices, aggregation_op
  
      # Parsing of MIN/MAX.
      if aggregation_op_index in (1, 2):
          operators = {2: min, 1: max}
          values = [
              (table["rows"][i][j], index)
              for index, (i, j) in enumerate(indices)
          ]
          reduced = functools.reduce(
              operators[sql_query["agg"]], values
          )
  
          ret = [indices[reduced[1]]]
          return ret, _Aggregation.NONE
  
      return indices, aggregation_op
  
imports: import dataclasses
  import enum
  import functools
  import math
  import re
  from typing import Any, List, Text
label_desc: Use image node_id 10 for calling a global function with example usage: _get_answer_coordinates(table, sql_query) and returns: indices,  aggregation_op, list,  aggregation_op, indices,  aggregation_op, ret,  _Aggregation