code: def evaluate(device, graph, edge_split, model, batch_size):
      """Evaluate the model on validation and test sets."""
      model.eval()
      evaluator = Evaluator(name="ogbl-citation2")
  
      # Compute node embeddings for the entire graph.
      node_emb = model.inference(graph, device, batch_size)
      results = []
  
      # Loop over both validation and test sets.
      for split in ["valid", "test"]:
          src = edge_split[split]["source_node"].to(node_emb.device)
          dst = edge_split[split]["target_node"].to(node_emb.device)
          neg_dst = edge_split[split]["target_node_neg"].to(
              node_emb.device
          )
  
          # Compute MRR values for the current split.
          results.append(
              compute_mrr(
                  model, evaluator, node_emb, src, dst, neg_dst, device
              )
          )
      return results
  
imports: import argparse
  import time
  import dgl
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, negative_sampler, NeighborSampler
  from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(device, graph, edge_split, model, batch_size) and returns: results