Suppose you have the following function and another function — moo(m) (not shown) — that runs in O(log n). What is the Big‑O runtime of the goo function shown below? def goo(widgets: list[Widget]) -> None:    for x in widgets:        moo(x)

Consider the following “odd” implementation of a list contains method: def bizarre_contains(a: list[float], key: float) -> bool:    for i in range(0, len(a)):        if key in a:           return True     return False What’s the Big O runtime for bizzare_contains?

You are adding an item to the middle of a python list. What’s the Big O?

The root node of a binary tree is any node in which ______________

Which sorting algorithm sorts selects the next item, finds and inserts it into the correct position in the sorted sequence of the list? 

Given an ordered list with 15 elements, how many elements must be visited in the worst case of binary search?

Consider the following recursive function: def r(xs: list[int]) -> list[int]:    match xs:        case []:            return []        case [x]:            return [x]        case [first, second, *rest]:            return [first] + r(rest) + [second] What list will r([1, 2, 3, 4]) return?

The function at_least returns True if at least two values in a list of booleans are True. Provide the Big‑O running time for the following function. def at_least( arr: list[bool] ) -> None:    count = 0    for i in range(0, len(arr)):        if arr[i] == True:            count += 1    return count >= 2

Consider the following recursive function: def p(xs: list[int]) -> list[int]:    match xs:        case []:            return []        case [x]:            return [x]        case [first, second, *rest]:            return p(rest) + [first, second] What list will p([1, 2, 3, 4, 5]) return?

Suppose you want a recursive function, product(xs: list[int]) -> int, that multiplies all the numbers it’s passed. Imagine the implementation — what do you suppose the base case for the product(..) function should return if [] is matched?