code: def _process_mentions_for_model(
      context_key,
      mentions,
      tokenizer,
      max_seq_length,
      top_k,
      silent,
      start_token="[unused0]",
      end_token="[unused1]",
      debug=False,
      tagged=True,
      sentences=None,
      candidates_key="candidates",
      gold_key="gold_pos",
      logger=None,
  ):
      processed_mentions = []
  
      if debug:
          mentions = mentions[:200]
  
      max_sub_seq_length = (max_seq_length - 3) // 2
  
      if silent:
          iter_ = mentions
      else:
          iter_ = tqdm(mentions)
  
      for idx, mention in enumerate(iter_):
          # if sentences is not none that means that we are processing end2end data for inference
          if sentences is not None:
              mention[
                  context_key
              ] = BertReranker._get_mention_context_end2end(
                  mention, sentences
              )
  
          context_tokens = (
              BertReranker._get_context_token_representation(
                  context_key,
                  mention,
                  tokenizer,
                  max_sub_seq_length,
                  start_token,
                  end_token,
              )
          )
  
          candidates = mention[candidates_key]
          candidate_features = []
  
          for candidate in candidates[:top_k]:
              candidate_desc = " ".join(candidate["sentences"])
  
              candidate_obj = (
                  BertReranker._get_candidate_representation(
                      context_tokens,
                      candidate_desc,
                      tokenizer,
                      max_seq_length,
                      max_sub_seq_length,
                  )
              )
              candidate_features.append(candidate_obj)
  
          entity_mask = [1] * len(candidate_features) + [0] * (
              top_k - len(candidate_features)
          )
  
          if len(candidates) < top_k:
              candidate_desc = ""
              padding_candidate_obj = (
                  BertReranker._get_candidate_representation(
                      context_tokens,
                      candidate_desc,
                      tokenizer,
                      max_seq_length,
                      max_sub_seq_length,
                  )
              )
              for _ in range(top_k - len(candidates)):
                  candidate_features.append(padding_candidate_obj)
  
          assert len(candidate_features) == top_k
          assert len(entity_mask) == top_k
  
          if sentences is not None:
              processed_mentions.append(
                  {
                      "candidate_features": candidate_features,
                      "mention_idx": idx,
                      "entity_mask": entity_mask,
                  }
              )
          else:
              label = mention[gold_key] - 1
              processed_mentions.append(
                  {
                      "candidate_features": candidate_features,
                      "label": label,
                      "entity_mask": entity_mask,
                  }
              )
  
      all_input_ids = torch.tensor(
          BertReranker._select_field(processed_mentions, "input_ids"),
          dtype=torch.long,
      )
      all_input_mask = torch.tensor(
          BertReranker._select_field(processed_mentions, "input_mask"),
          dtype=torch.long,
      )
      all_segment_ids = torch.tensor(
          BertReranker._select_field(processed_mentions, "segment_ids"),
          dtype=torch.long,
      )
      all_entity_masks = torch.tensor(
          [s["entity_mask"] for s in processed_mentions],
          dtype=torch.float,
      )
  
      data = {
          "all_input_ids": all_input_ids,
          "all_input_mask": all_input_mask,
          "all_segment_ids": all_segment_ids,
          "all_entity_masks": all_entity_masks,
      }
  
      if sentences is not None:
          all_mention_indices = torch.tensor(
              [s["mention_idx"] for s in processed_mentions],
              dtype=torch.long,
          )
          data["all_mention_indices"] = all_mention_indices
          tensor_data = TensorDataset(
              all_input_ids,
              all_input_mask,
              all_segment_ids,
              all_mention_indices,
              all_entity_masks,
          )
      else:
          all_label = torch.tensor(
              [s["label"] for s in processed_mentions], dtype=torch.long
          )
          data["all_label"] = all_label
          tensor_data = TensorDataset(
              all_input_ids,
              all_input_mask,
              all_segment_ids,
              all_label,
              all_entity_masks,
          )
  
      if logger != None:
          logger.info(
              "all_input_ids shape: {}".format(all_input_ids.shape)
          )
          logger.info(
              "all_input_mask shape: {}".format(all_input_mask.shape)
          )
          logger.info(
              "all_segment_ids shape: {}".format(all_segment_ids.shape)
          )
          logger.info(
              "all_entity_masks shape: {}".format(
                  all_entity_masks.shape
              )
          )
          if sentences is not None:
              logger.info(
                  "all_mention_indices shape: {}".format(
                      all_mention_indices.shape
                  )
              )
          else:
              logger.info("all_label shape: {}".format(all_label.shape))
  
      return data, tensor_data
  
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 10 for calling the BertReranker obj's underlying member method code with example usage: obj._process_mentions_for_model(context_key, mentions, tokenizer, max_seq_length, top_k, silent, start_token, end_token, debug, tagged, sentences, candidates_key, gold_key, logger) and returns: data,  tensor_data