Task: NL2SQL (source dataset: Spider)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Database:
vehicle_rent

Tables:

Table: Customers
|   id | name      |   age |   membership_credit |
|-----:|:----------|------:|--------------------:|
|    1 | Griffiths |    26 |                 100 |
|    2 | Silluzio  |    34 |                1200 |
|    3 | Woodman   |    35 |                2000 |
|    4 | Poulter   |    63 |               43500 |
|    5 | Smith     |    45 |                5399 |


Table: Discount
|   id | name                   |   membership_credit |
|-----:|:-----------------------|--------------------:|
|    1 | no discount            |                   0 |
|    2 | 20% off                |                1000 |
|    3 | 40% off for over $6000 |                2000 |
|    4 | 50% off                |                4000 |
|    5 | 70% off                |              400000 |


Table: Vehicles
|   id | name                |   Model_year | Type_of_powertrain   |   Combined_fuel_economy_rate |   City_fuel_economy_rate |   Highway_fuel_economy_rate |   Cost_per_25_miles |   Annual_fuel_cost | Notes                    |
|-----:|:--------------------|-------------:|:---------------------|-----------------------------:|-------------------------:|----------------------------:|--------------------:|-------------------:|:-------------------------|
|    1 | Chevrolet Spark EV  |         2014 | Electric             |                          119 |                      128 |                         109 |                0.87 |                500 | See (1)                  |
|    2 | Honda Fit EV        |         2013 | hybrid               |                          118 |                      132 |                         105 |                0.87 |                500 | See (1)                  |
|    3 | Fiat 500e           |         2013 | Electric             |                          116 |                      122 |                         108 |                0.87 |                500 | See (1)                  |
|    4 | Nissan Leaf         |         2013 | Electric             |                          115 |                      129 |                         102 |                0.87 |                500 | See (1)                  |
|    5 | Mitsubishi i        |         2012 | hybrid               |                          112 |                      126 |                          99 |                0.9  |                550 | best selling of the year |
|    6 | Ford Focus Electric |         2012 | electric             |                          105 |                      110 |                          99 |                0.96 |                600 | See (2)                  |
|    7 | BMW ActiveE         |         2011 | Electric             |                          102 |                      107 |                          96 |                0.99 |                600 | See (1)                  |


Table: Renting_history
|   id |   customer_id |   discount_id |   vehicles_id |   total_hours |
|-----:|--------------:|--------------:|--------------:|--------------:|
|    1 |             1 |             1 |             2 |             1 |
|    2 |             2 |             2 |             5 |            10 |
|    3 |             3 |             3 |             7 |            24 |
|    4 |             4 |             4 |             3 |            24 |
|    5 |             1 |             1 |             5 |            36 |
|    6 |             2 |             2 |             1 |            24 |
|    7 |             5 |             4 |             4 |            72 |


Question: Return the average age for customers who have membership above the average across all customers.

db_id: vehicle_rent
tables: name: Customers
data: Customers.csv
name: Discount
data: Discount.csv
name: Vehicles
data: Vehicles.csv
name: Renting_history
data: Renting_history.csv
label: SELECT avg(age) FROM customers WHERE membership_credit  >  (SELECT avg(membership_credit) FROM customers)
sqlite_path: $MMTU_HOME/data_sqlite/spider/spider_data/test_database/vehicle_rent/vehicle_rent.sqlite