In this task, you are given commands (in terms of logical operations) to select relevant rows from the given table. Your job is to generate a natural language interpretation of the given command. 
 Here are the definitions of logical operators: 
 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 argument 1 is greater/less than argument 2 
 12. diff: returns the difference between two arguments 
 13. filter eq/not eq: returns the subview whose values under the header column is equal/not equal to argument 3 
 14. filter greater/less: returns the subview whose values under the header column is greater/less than argument 3 
 15. filter greater eq /less eq: returns the subview whose values under the header column is greater/less or equal than argument 3 
 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 argument 3 
 18. all greater/less: returns whether all the values under the header column are greater/less than argument 3 
 19. all greater eq/less eq: returns whether all the values under the header column are greater/less or equal to argument 3 
 20. most eq/not eq: returns whether most of the values under the header column are equal/not equal to argument 3 
 21. most greater/less: returns whether most of the values under the header column are greater/less than argument 3 
 22. most greater eq/less eq: returns whether most of the values under the header column are greater/less or equal to argument 3

Example Input: and { only { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } } ; eq { hop { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } ; season } ; 1941 - 42 } } = true
Example Output: select the rows whose winner record fuzzily matches to rangers . among these rows , select the rows whose runner  up record fuzzily matches to morton . there is only one such row in the table . the season record of this unqiue row is 1941  42 .

Example Input: eq { count { filter_eq { all_rows ; club ; real madrid } } ; 9 } = true
Example Output: select the rows whose club record fuzzily matches to real madrid . the number of such rows is 9 .

Example Input: and { greater { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } } ; and { eq { hop { filter_eq { all_rows ; city ; newcastle } ; erp ( analog / digital ) } ; 1200 kw 500 kw } ; eq { hop { filter_eq { all_rows ; city ; lismore } ; erp ( analog / digital ) } ; 200 kw 200 kw } } } = true
Example Output:
select the rows whose city record fuzzily matches to newcastle . take the erp  analog  digital  record of this row . select the rows whose city record fuzzily matches to lismore . take the erp  analog  digital  record of this row . the first record is greater than the second record . the erp  analog  digital  record of the first row is 1200 kw 500 kw . the erp  analog  digital  record of the second row is 200 kw 200 kw .