Part A (category by average time for videos): You receive a list of pin engagement rows and a category map. pins = [ {"pin_id": 10, "category_id": 1, "time_spent": 18.0, "pin_format": "video"}, {"pin_id": 11, "category_id": 2, "time_spent": 6.0, "pin_format": "static"}, {"pin_id": 12, "category_id": 1, "time_spent": 22.0, "pin_format": "video"}, {"pin_id": 13, "category_id": 3, "time_spent": 15.0, "pin_format": "video"}, {"pin_id": 14, "category_id": 2, "time_spent": None, "pin_format": "video"} ] category_map = {1: "Fashion", 2: "Food", 3: "DIY"} Task: Among video pins only, compute the category_name with the highest average time_spent. Ignore records where time_spent is None. Break ties by category_name alphabetically. Return (category_name, average_time) with average_time rounded to 2 decimals. Implement an O(n) solution using only Python 3.10+ standard library. Part B (average unique pins per user): You receive a mapping of user -> list of pin_ids they interacted with. user_pins = {"user1": [1,2,2], "user2": [1,2,3], "user3": []} Task: Compute the average number of unique pin_ids per user. Empty lists count as 0. Return a float rounded to 2 decimals. Aim for O(total_items) time and O(U) extra space, where U is number of users.