code: def get_topk_predictions(
      reranker,
      train_dataloader,
      candidate_pool,
      cand_encode_list,
      silent,
      logger,
      top_k=10,
      is_zeshel=False,
      save_predictions=False,
  ):
      reranker.model.eval()
      device = reranker.device
      logger.info("Getting top %d predictions." % top_k)
      if silent:
          iter_ = train_dataloader
      else:
          iter_ = tqdm(train_dataloader)
  
      nn_context = []
      nn_candidates = []
      nn_labels = []
      nn_worlds = []
      stats = {}
  
      if is_zeshel:
          world_size = len(WORLDS)
      else:
          # only one domain
          world_size = 1
          candidate_pool = [candidate_pool]
          cand_encode_list = [cand_encode_list]
  
      logger.info("World size : %d" % world_size)
  
      for i in range(world_size):
          stats[i] = Stats(top_k)
  
      oid = 0
      for step, batch in enumerate(iter_):
          batch = tuple(t.to(device) for t in batch)
          context_input, _, srcs, label_ids = batch
          src = srcs[0].item()
          scores = reranker.score_candidate(
              context_input,
              None,
              cand_encs=cand_encode_list[src].to(device),
          )
          values, indicies = scores.topk(top_k)
          old_src = src
          for i in range(context_input.size(0)):
              oid += 1
              inds = indicies[i]
  
              if srcs[i] != old_src:
                  src = srcs[i].item()
                  # not the same domain, need to re-do
                  new_scores = reranker.score_candidate(
                      context_input[[i]],
                      None,
                      cand_encs=cand_encode_list[src].to(device),
                  )
                  _, inds = new_scores.topk(top_k)
                  inds = inds[0]
  
              pointer = -1
              for j in range(top_k):
                  if inds[j].item() == label_ids[i].item():
                      pointer = j
                      break
              stats[src].add(pointer)
  
              if pointer == -1:
                  continue
  
              if not save_predictions:
                  continue
  
              # add examples in new_data
              cur_candidates = candidate_pool[src][inds]
              nn_context.append(context_input[i].cpu().tolist())
              nn_candidates.append(cur_candidates.cpu().tolist())
              nn_labels.append(pointer)
              nn_worlds.append(src)
  
      res = Stats(top_k)
      for src in range(world_size):
          if stats[src].cnt == 0:
              continue
          if is_zeshel:
              logger.info("In world " + WORLDS[src])
          output = stats[src].output()
          logger.info(output)
          res.extend(stats[src])
  
      logger.info(res.output())
  
      nn_context = torch.LongTensor(nn_context)
      nn_candidates = torch.LongTensor(nn_candidates)
      nn_labels = torch.LongTensor(nn_labels)
      nn_data = {
          "context_vecs": nn_context,
          "candidate_vecs": nn_candidates,
          "labels": nn_labels,
      }
  
      if is_zeshel:
          nn_data["worlds"] = torch.LongTensor(nn_worlds)
  
      return nn_data
  
imports: import json
  import logging
  import torch
  from tqdm import tqdm
  import blink.candidate_ranking.utils
  from blink.biencoder.zeshel_utils import WORLDS, Stats
label_desc: Use image node_id 1 for calling a global function with example usage: get_topk_predictions(reranker, train_dataloader, candidate_pool, cand_encode_list, silent, logger, top_k, is_zeshel, save_predictions) and returns: nn_data