code: def evaluate(
      args, model, graph, features, all_nodes_set, valid_set, test_set
  ):
      """Evaluate the model on validation and test sets."""
      model.eval()
      evaluator = Evaluator(name="ogbl-citation2")
  
      # Since we need to use all neghborhoods for evaluation, we set the fanout
      # to -1.
      args.fanout = [-1]
      dataloader = create_dataloader(
          args, graph, features, all_nodes_set, is_train=False
      )
  
      # Compute node embeddings for the entire graph.
      node_emb = model.inference(
          graph, features, dataloader, args.device
      )
      results = []
  
      # Loop over both validation and test sets.
      for split in [valid_set, test_set]:
          # Unpack the item set.
          src = split._items[0][:, 0].to(node_emb.device)
          dst = split._items[0][:, 1].to(node_emb.device)
          neg_dst = split._items[1].to(node_emb.device)
  
          # Compute MRR values for the current split.
          results.append(
              compute_mrr(
                  args, model, evaluator, node_emb, src, dst, neg_dst
              )
          )
      return results
  
imports: import argparse
  import time
  from functools import partial
  import dgl.graphbolt
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from ogb.linkproppred import Evaluator
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(args, model, graph, features, all_nodes_set, valid_set, test_set) and returns: results