code: def _load_candidates(
      entity_catalogue,
      entity_encoding,
      faiss_index=None,
      index_path=None,
      logger=None,
  ):
      # only load candidate encoding if not using faiss index
      if faiss_index is None:
          candidate_encoding = torch.load(entity_encoding)
          indexer = None
      else:
          if logger:
              logger.info("Using faiss index to retrieve entities.")
          candidate_encoding = None
          assert index_path is not None, "Error! Empty indexer path."
          if faiss_index == "flat":
              indexer = DenseFlatIndexer(1)
          elif faiss_index == "hnsw":
              indexer = DenseHNSWFlatIndexer(1)
          else:
              raise ValueError(
                  "Error! Unsupported indexer type! Choose from flat,hnsw."
              )
          indexer.deserialize_from(index_path)
  
      # load all the 5903527 entities
      title2id = {}
      id2title = {}
      id2text = {}
      wikipedia_id2local_id = {}
      local_idx = 0
      with open(entity_catalogue, "r") as fin:
          lines = fin.readlines()
          for line in lines:
              entity = json.loads(line)
  
              if "idx" in entity:
                  split = entity["idx"].split("curid=")
                  if len(split) > 1:
                      wikipedia_id = int(split[-1].strip())
                  else:
                      wikipedia_id = entity["idx"].strip()
  
                  assert wikipedia_id not in wikipedia_id2local_id
                  wikipedia_id2local_id[wikipedia_id] = local_idx
  
              title2id[entity["title"]] = local_idx
              id2title[local_idx] = entity["title"]
              id2text[local_idx] = entity["text"]
              local_idx += 1
      return (
          candidate_encoding,
          title2id,
          id2title,
          id2text,
          wikipedia_id2local_id,
          indexer,
      )
  
imports: import argparse
  import json
  import sys
  from tqdm import tqdm
  import logging
  import torch
  import numpy
  from colorama import init
  from termcolor import colored
  import blink.ner
  from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
  from blink.biencoder.biencoder import BiEncoderRanker, load_biencoder
  from blink.crossencoder.crossencoder import CrossEncoderRanker, load_crossencoder
  from blink.biencoder.data_process import process_mention_data, get_candidate_representation
  import blink.candidate_ranking.utils
  from blink.crossencoder.train_cross import modify, evaluate
  from blink.crossencoder.data_process import prepare_crossencoder_data
  from blink.indexer.faiss_indexer import DenseFlatIndexer, DenseHNSWFlatIndexer
label_desc: Use image node_id 4 for calling a global function with example usage: _load_candidates(entity_catalogue, entity_encoding, faiss_index, index_path, logger) and returns: candidate_encoding,  title2id,  id2title,  id2text,  wikipedia_id2local_id,  indexer