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.

select the rows whose equatorial bulge record is greater than 1000 . among these rows , select the rows whose polar diameter record is greater than 100,000 . the number of such rows is 2 .
eq { count { filter_greater { filter_greater { all_rows ; equatorial bulge ; 1000 } ; polar diameter ; 100,000 } } ; 2 }

select the rows whose name record fuzzily matches to historic federal reserve bank . take the floors record of this row . select the rows whose name record fuzzily matches to kansas city power and light building . take the floors record of this row . the first record is less than the second record . the floors record of the first row is 16 . the floors record of the second row is 34 .
and { less { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } } ; and { eq { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; 16 } ; eq { hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } ; 34 } } }

select the rows whose date record is less than 1914 . the sum of the no built record of these rows is 910 .
round_eq { sum { filter_less { all_rows ; date ; 1914 } ; no built } ; 910 }