code: def rerank(self, mentions, sentences):
      model = self.model
      tokenizer = self.tokenizer
      p = self.parameters
      device = self.device
  
      data, tensor_data = BertReranker._process_mentions_for_model(
          p["context_key"],
          mentions,
          tokenizer,
          p["max_seq_length"],
          p["top_k"],
          p["silent"],
          sentences=sentences,
      )
  
      sampler = SequentialSampler(tensor_data)
      dataloader = DataLoader(
          tensor_data,
          sampler=sampler,
          batch_size=p["evaluation_batch_size"],
      )
  
      softmax = torch.nn.Softmax(dim=1)
  
      for (
          input_ids,
          input_mask,
          segment_ids,
          mention_ids,
          entity_mask,
      ) in tqdm(dataloader, desc="Inferring"):
          input_ids = input_ids.to(device)
          input_mask = input_mask.to(device)
          segment_ids = segment_ids.to(device)
          mention_ids = mention_ids.numpy()
          entity_mask = entity_mask.to(device)
  
          with torch.no_grad():
              logits = self.model(
                  input_ids,
                  segment_ids,
                  input_mask,
                  entity_mask=entity_mask,
              )[0]
              probs = softmax(logits)
  
          logits = logits.detach().cpu().numpy()
          probs = probs.detach().cpu().numpy()
  
          predictions = np.argmax(logits, axis=1)
  
          for idx, mention_idx in enumerate(mention_ids):
              pred = predictions[idx].item()
              mentions[mention_idx]["predicted_candidate_idx"] = pred
              mentions[mention_idx][
                  "prob_assigned_to_candidate"
              ] = probs[idx][pred].item()
  
      return mentions
  
imports: import torch
  import os
  import numpy
  from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig, BertModel
  from pytorch_transformers.tokenization_bert import BertTokenizer
  from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from tqdm import tqdm
  from pytorch_transformers.optimization import AdamW, WarmupLinearSchedule
  from pytorch_transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
label_desc: Use image node_id 2 for calling the BertReranker obj's underlying member method code with example usage: obj.rerank(mentions, sentences) and returns: mentions