code: def compute_mrr(args, model, evaluator, node_emb, src, dst, neg_dst):
      """Compute the Mean Reciprocal Rank (MRR) for given source and destination
      nodes.
  
      This function computes the MRR for a set of node pairs, dividing the task
      into batches to handle potentially large graphs.
      """
      rr = torch.zeros(src.shape[0])
      # Loop over node pairs in batches.
      for start in tqdm.trange(
          0, src.shape[0], args.eval_batch_size, desc="Evaluate"
      ):
          end = min(start + args.eval_batch_size, src.shape[0])
  
          # Concatenate positive and negative destination nodes.
          all_dst = torch.cat(
              [dst[start:end, None], neg_dst[start:end]], 1
          )
  
          # Fetch embeddings for current batch of source and destination nodes.
          h_src = node_emb[src[start:end]][:, None, :].to(args.device)
          h_dst = (
              node_emb[all_dst.view(-1)]
              .view(*all_dst.shape, -1)
              .to(args.device)
          )
  
          # Compute prediction scores using the model.
          pred = model.predictor(h_src * h_dst).squeeze(-1)
  
          # Evaluate the predictions to obtain MRR values.
          input_dict = {
              "y_pred_pos": pred[:, 0],
              "y_pred_neg": pred[:, 1:],
          }
          rr[start:end] = evaluator.eval(input_dict)["mrr_list"]
      return rr.mean()
  
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 2 for calling a global function with example usage: compute_mrr(args, model, evaluator, node_emb, src, dst, neg_dst) and returns: rr