## Problem Implement `schedule_gpu_nodes(nodes, request_gpus, delete_name)`. Each node is `{name, gpus, running_pods}` and each pod is `{name, required_gpus}`. Inputs are well formed: pod names and node names are unique, and existing pods do not exceed node capacity. Return two results: 1. `eligible`: every node with at least `request_gpus` free GPUs, represented as `[node_name, free_gpus]` in node input order. 2. `reschedule`: remove the node named `delete_name` and place all of its pods on the remaining nodes without moving existing pods. Return `[pod_name, destination_node]` pairs in the drained node's pod order. If no complete placement exists, return `null`. When several placements work, return the lexicographically smallest destination-node sequence. ## Constraints - Up to 100 nodes for eligibility queries. - The drained node has at most 12 pods and at most 12 remaining nodes for exact rescheduling. - GPU counts are nonnegative integers. - A pod must fit entirely on one node. ## Example If node `A` has capacity 8 with pods using 6 GPUs, node `B` has capacity 8 with pods using 4 GPUs, and node `C` is empty with capacity 8, then a request for 2 GPUs lists all three nodes with free capacities `2`, `4`, and `8`. Draining `A` must place all of its pods on `B` and `C` or return `null`. ## Hint Eligibility is direct accounting. Exact rescheduling is bin packing: sort or prioritize difficult pods for search, prune when remaining capacity is insufficient, and skip symmetric states while still honoring the required deterministic output. ## Interview Follow-ups - Compare greedy, first-fit decreasing, backtracking, and branch-and-bound. - Scale the rescheduler when an exact search is too expensive. - Make scheduling safe under concurrent pod creation.