unpivot (wide to long) with dynamic column names - azure-data-explorer

Question

I need a function wide_to_long that turns a wide table into a long table and that accepts an argument id_vars for which the values have to be repeated (see example).
Sample input
let T_wide = datatable(name: string, timestamp: datetime, A: int, B: int) [
'abc','2022-01-01 12:00:00',1,2,
'def','2022-01-01 13:00:00',3,4
];
Desired output
Calling wide_to_long(T_wide, dynamic(['name', 'timestamp'])) should produce the following table.
let T_long = datatable(name: string, timestamp: datetime, variable: string, value: int) [
'abc','2022-01-01 12:00:00','A',1,
'abc','2022-01-01 12:00:00','B',2,
'def','2022-01-01 13:00:00','A',3,
'def','2022-01-01 13:00:00','B',4
];
Attempt
I've come pretty far with the following code.
let wide_to_long = (T:(*), id_vars: dynamic) {
// get names of keys to remove later
let all_columns = toscalar(T | getschema | summarize make_list(ColumnName));
let remove = set_difference(all_columns, id_vars);
// expand columns not contained in id_vars
T
| extend packed1 = pack_all()
| extend packed1 = bag_remove_keys(packed1, id_vars)
| mv-expand kind=array packed1
| extend variable = packed1[0], value = packed1[1]
// remove unwanted columns
| project packed2 = pack_all()
| project packed2 = bag_remove_keys(packed2, remove)
| evaluate bag_unpack(packed2)
| project-away packed1
};
The problems are that the solution feels clunky (is there a better way?) and the columns in the result are ordered randomly. The second issue is minor, but annoying.