Check feasibility of AI course schedule

You are designing a learning path for `n` AI-related courses, labeled from `0` to `n - 1`. Some courses have prerequisites. For example, to take course `b`, you may first need to finish course `a`. This can be represented as a directed edge `a -> b`. You are given: - An integer `n`, the number of courses. - A list `prerequisites` of pairs `[a, b]`, where each pair means: **to take course `b`, you must first complete course `a`**. You need to determine whether it is possible to finish **all** courses. Formally, model the courses as a directed graph: - Each course is a node. - Each prerequisite pair `[a, b]` is a directed edge from `a` to `b`. If the graph contains a cycle (e.g., `0 -> 1 -> 2 -> 0`), then there is no way to complete all courses because each course in the cycle depends on another in the cycle. ### Task Write an algorithm that: - Returns `true` if it is possible to complete all `n` courses given the prerequisites. - Returns `false` if it is **not** possible (i.e., if there is at least one cycle in the prerequisite graph). **Constraints** (typical interview constraints; you may assume something like): - `1 <= n <= 10^5`. - `0 <= prerequisites.length <= 10^5`. - `0 <= a, b < n` for every pair `[a, b]`. Your algorithm should run in **O(n + m)** time, where `m = prerequisites.length`, and use **O(n + m)** extra space. You may use any standard graph traversal technique (e.g., DFS or BFS/topological sort).