What is the worst-time complexity of the following function if the input list is of size n?

def mystery(my_list: ArrayList) -> None:
    my_queue = Queue(len(my_list) * 2)
    for item in my_list:
        my_queue.append(item)
        my_queue.append(item)
    while not my_queue.is_empty():
        print(my_queue.serve())

If we need to frequently use the difference() operation for two sets, what implementation of the Set ADT would be "cheaper" in terms of the time complexity of this operation?

Stack pops items in the following order:

What does the following code do?

def mystery(a_list, b_list):
    my_stack = Stack(len(a_list)+len(b_list))
    my_list = List(len(a_list)+len(b_list))
    
    for item in a_list:
        my_stack.push(item)
    
    for item in b_list:
        my_stack.push(item)
    while not my_stack.is_empty():
        my_list.append(my_stack.pop())
    return my_list

Order these operations from fastest to slowest based on their time complexity. Recall that the complexity of some of these operations may be measured with respect to different inputs.

What in the following are Set ADT operations?

Given that there are

Given a numeric algorithm taking an arbitrary integer n as input, with respect to what should its complexity be analysed?

Estimate the complexity of the following function in terms of the input integer n:

def mystery(n: int) -> int:
    result = 0
    for i in range(n):
        for j in range(i):
            result += i * j
     return result

Is Insertion Sort Incremental?