Design an in-memory structure that tracks item frequencies and answers top-K queries. You will implement a single driver function `solution(operations)` that replays a list of operations against the structure and returns the result of every `topK` query, in order. Each operation is a 2-element list: - `["add", x]` — observe item `x` (increments its frequency by 1, creating it if absent). - `["inc", x]` — increment the frequency of `x` by 1 (creating it if absent). Semantically identical to `add`. - `["dec", x]` — decrement the frequency of `x` by 1; when its frequency reaches 0 the item is deleted. A `dec` on an unknown item is a no-op. - `["topK", k]` — return the `k` items with the highest frequency. **Ties are broken by most-recent update** (the item updated more recently comes first). If fewer than `k` items exist, return all of them. Return a list containing the result list of each `topK` operation, in the order the `topK` operations appear. A recency 'update' is any `add`, `inc`, or any `dec` that does NOT delete the item. The intended efficient design uses a hash map of item -> node plus a doubly linked list of frequency buckets (each bucket a recency-ordered list of items) to get O(1) amortized updates and O(k) retrieval; the reference here uses a clear simulation, but your console solution may implement the bucket-list design. Example: operations `[["add","a"],["add","b"],["inc","a"],["topK",2]]` → `[["a","b"]]` because `a` has frequency 2 and `b` has frequency 1.