In this task, you are given a natural language interpretation of commands (consist of logical operations) to select relevant rows from the given table. Your job is to generate command (in terms of logical operations) from given natural language interpretation. Define body (contains a collection of statements that define what the this logical operator does) of each logical operator between '{}' parenthesis. Here are the definitions of logical operators that you can use while generating command: 
 1. count: returns the number of rows in the view. 
 2. only: returns whether there is exactly one row in the view. 
 3. hop: returns the value under the header column of the row. 
 4. and: returns the boolean operation result of two arguments. 
 5. max/min/avg/sum: returns the max/min/average/sum of the values under the header column. 
 6. nth_max/nth_min: returns the n-th max/n-th min of the values under the header column. 
 7. argmax/argmin: returns the row with the max/min value in header column. 
 8. nth_argmax/nth_argmin: returns the row with the n-th max/min value in header column. 
 9. eq/not_eq: returns if the two arguments are equal. 
 10. round_eq: returns if the two arguments are roughly equal under certain tolerance. 
 11. greater/less: returns if the first argument is greater/less than the second argument. 
 12. diff: returns the difference between two arguments. 
 13. filter_eq/ filter_not_eq: returns the subview whose values under the header column is equal/not equal to the third argument. 
 14. filter_greater/filter_less: returns the subview whose values under the header column is greater/less than the third argument. 
 15. filter_greater_eq /filter_less_eq: returns the subview whose values under the header column is greater/less or equal than the third argument. 
 16. filter_all: returns the view itself for the case of describing the whole table 
 17. all_eq/not_eq: returns whether all the values under the header column are equal/not equal to the third argument. 
 18. all_greater/less: returns whether all the values under the header column are greater/less than the third argument. 
 19. all_greater_eq/less_eq: returns whether all the values under the header column are greater/less or equal to the third argument. 
 20. most_eq/not_eq: returns whether most of the values under the header column are equal/not equal to the third argument. 
 21. most_greater/less: returns whether most of the values under the header column are greater/less than the third argument. 
 22. most_greater_eq/less_eq: returns whether most of the values under the header column are greater/less or equal to the third argument.

Input: Consider Input: select the rows whose english ( streamline ) record fuzzily matches to unknown . among these rows , select the rows whose english ( pioneer / geneon ) record fuzzily matches to richard cansino . the number of such rows is 2 .

Output: eq { count { filter_eq { filter_eq { all_rows ; english ( streamline ) ; unknown } ; english ( pioneer / geneon ) ; richard cansino } } ; 2 }


Input: Consider Input: the average of the gold record of all rows is 2.86 .

Output: round_eq { avg { all_rows ; gold } ; 2.86 }


Input: Consider Input: select the rows whose date record fuzzily matches to november 18 , 1962 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 16 , 1962 . take the attendance record of this row . the first record is greater than the second record . the attendance record of the first row is 12500 . the attendance record of the second row is 8000 .


Output: and { greater { hop { filter_eq { all_rows ; date ; november 18 , 1962 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 16 , 1962 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; november 18 , 1962 } ; attendance } ; 12500 } ; eq { hop { filter_eq { all_rows ; date ; december 16 , 1962 } ; attendance } ; 8000 } } }