}
	}
}

func scanIntoMap(mapValue map[string]interface{}, values []interface{}, columns []string) {
	for idx, column := range columns {
		if reflectValue := reflect.Indirect(reflect.Indirect(reflect.ValueOf(values[idx]))); reflectValue.IsValid() {
			mapValue[column] = reflectValue.Interface()
			if valuer, ok := mapValue[column].(driver.Valuer); ok {
				mapValue[column], _ = valuer.Value()

	case reflect.Map:
		results = [][]interface{}{make([]interface{}, len(fields))}
		for idx, field := range fields {
			mapValue := reflectValue.MapIndex(reflect.ValueOf(field.DBName))
			if mapValue.IsZero() {
				mapValue = reflectValue.MapIndex(reflect.ValueOf(field.Name))
			}
			results[0][idx] = mapValue.Interface()
		}

		dataResults[utils.ToStringKey(results[0]...)] = []reflect.Value{reflectValue}
	case reflect.Struct:

                    if (value instanceof Map) {
                        @SuppressWarnings("unchecked")
                        final Map<String, Object> mapValue = (Map<String, Object>) value;
                        return toLowerHyphen(mapValue);
                    }
                    return e.getValue();
                }));
    }

    /**
     * Sets the user check interval.
     *

            WebApiUtil.setObject("listValue", list);

            java.util.Map<String, String> map = new java.util.HashMap<>();
            map.put("key", "value");
            WebApiUtil.setObject("mapValue", map);

        } catch (Exception e) {
            fail("setObject should handle different object types: " + e.getMessage());
        }
    }

    @Test

*   Calling ops with a python constants or numpy values is now consistent with
    tf.convert_to_tensor behavior. This avoids operations like tf.reshape
    truncating inputs such as from int64 to int32.
*   Adds `tf.sparse.map_values` to apply a function to the `.value`s of
    `SparseTensor` arguments.
*   The Python bitwise operators for `Tensor` (`__and__`, `__or__`, `__xor__`
    and `__invert__` now support non-`bool` arguments and apply the