code: def evaluate(
      reranker,
      eval_dataloader,
      params,
      device,
      logger,
      cand_encs=None,
      faiss_index=None,
      get_losses=False,
  ):
      reranker.model.eval()
      if params["silent"]:
          iter_ = eval_dataloader
      else:
          iter_ = tqdm(eval_dataloader, desc="Evaluation")
  
      results = {}
  
      eval_num_correct = 0.0
      eval_num_p = 0.0
      eval_num_g = 0.0
      nb_eval_examples = 0
      nb_eval_steps = 0
      overall_loss = 0.0
  
      if cand_encs is not None and not params["freeze_cand_enc"]:
          torch.cuda.empty_cache()
          cand_encs = cand_encs.to(device)
  
      for step, batch in enumerate(iter_):
          batch = tuple(t.to(device) for t in batch)
          context_input = batch[0]
          candidate_input = batch[1]
          # (bs, num_actual_spans)
          label_ids = (
              batch[2].cpu().numpy()
              if params["freeze_cand_enc"]
              else None
          )
          if params["debug"] and label_ids is not None:
              label_ids[label_ids > 199] = 199
          mention_idx = batch[-2].cpu().numpy()
          mention_idx_mask = batch[-1].cpu().numpy()
  
          with torch.no_grad():
              # evaluate with joint mention detection
              if params["freeze_cand_enc"]:
                  context_outs = reranker.encode_context(
                      context_input,
                      num_cand_mentions=50,
                      topK_threshold=-3.5,
                  )
                  embedding_context = (
                      context_outs["mention_reps"].cpu().numpy()
                  )
                  pred_mention_mask = (
                      context_outs["mention_masks"].cpu().numpy()
                  )
                  chosen_mention_bounds = (
                      context_outs["mention_bounds"].cpu().numpy()
                  )
                  embedding_ctxt = embedding_context[pred_mention_mask]
                  # do faiss search for closest entity
                  # DIM (all_pred_mentions_batch, num_cand_entities); (all_pred_mentions_batch, num_cand_entities)
                  (
                      top_cand_logits_shape,
                      top_cand_indices_shape,
                  ) = faiss_index.search_knn(embedding_ctxt, 10)
                  top_cand_logits = np.zeros(
                      (
                          pred_mention_mask.shape[0],
                          pred_mention_mask.shape[1],
                          10,
                      ),
                      dtype=np.float,
                  )
                  top_cand_indices = np.zeros_like(
                      pred_mention_mask, dtype=np.int
                  )
                  top_cand_logits[
                      pred_mention_mask
                  ] = top_cand_logits_shape
                  top_cand_indices[
                      pred_mention_mask
                  ] = top_cand_indices_shape[:, 0]
                  scores = (
                      np.log(softmax(top_cand_logits, -1))
                      + torch.sigmoid(
                          context_outs["mention_logits"].unsqueeze(-1)
                      )
                      .log()
                      .cpu()
                      .numpy()
                  )[:, :, 0]
                  tmp_num_correct = 0.0
                  tmp_num_p = 0.0
                  tmp_num_g = 0.0
                  for i, ex in enumerate(top_cand_indices):
                      gold_mb = mention_idx[i][mention_idx_mask[i]]
                      gold_label_ids = label_ids[i][mention_idx_mask[i]]
                      overall_score_mask = (
                          scores[i][pred_mention_mask[i]] > -2.5
                      )
                      pred_mb = chosen_mention_bounds[i][
                          pred_mention_mask[i]
                      ][overall_score_mask]
                      pred_label_ids = ex[pred_mention_mask[i]][
                          overall_score_mask
                      ]
                      gold_triples = [
                          (
                              str(gold_label_ids[j]),
                              gold_mb[j][0],
                              gold_mb[j][1],
                          )
                          for j in range(len(gold_mb))
                      ]
                      pred_triples = [
                          (
                              str(pred_label_ids[j]),
                              pred_mb[j][0],
                              pred_mb[j][1],
                          )
                          for j in range(len(pred_mb))
                      ]
                      (
                          num_overlap_weak,
                          _,
                      ) = entity_linking_tp_with_overlap(
                          gold_triples, pred_triples
                      )
                      tmp_num_correct += num_overlap_weak
                      tmp_num_p += float(len(pred_triples))
                      tmp_num_g += float(len(gold_triples))
                  text_encs = embedding_context
              else:
                  (
                      loss,
                      logits,
                      mention_logits,
                      mention_bounds,
                  ) = reranker(
                      context_input,
                      candidate_input,
                      cand_encs=cand_encs,
                      gold_mention_bounds=batch[-2],
                      gold_mention_bounds_mask=batch[-1],
                      return_loss=True,
                  )
                  logits = logits.cpu().numpy()
                  # Using in-batch negatives, the label ids are diagonal
                  label_ids = torch.LongTensor(
                      torch.arange(logits.shape[0])
                  )
                  label_ids = label_ids.cpu().numpy()
                  tmp_num_correct = utils.accuracy(logits, label_ids)
                  tmp_num_p = len(batch[-2][batch[-1]])
                  tmp_num_g = len(batch[-2][batch[-1]])
  
                  overall_loss += loss
  
          eval_num_correct += tmp_num_correct
          eval_num_p += tmp_num_p
          eval_num_g += tmp_num_g
  
          nb_eval_steps += 1
  
      if cand_encs is not None:
          cand_encs = cand_encs.to("cpu")
          torch.cuda.empty_cache()
  
      if nb_eval_steps > 0 and overall_loss > 0:
          normalized_overall_loss = overall_loss / nb_eval_steps
          logger.info("Overall loss: %.5f" % normalized_overall_loss)
      if eval_num_p > 0:
          normalized_eval_p = eval_num_correct / eval_num_p
      else:
          normalized_eval_p = 0.0
      if eval_num_g > 0:
          normalized_eval_r = eval_num_correct / eval_num_g
      else:
          normalized_eval_r = 0.0
      logger.info("Precision: %.5f" % normalized_eval_p)
      logger.info("Recall: %.5f" % normalized_eval_r)
      if normalized_eval_p + normalized_eval_r == 0:
          f1 = 0
      else:
          f1 = (
              2
              * normalized_eval_p
              * normalized_eval_r
              / (normalized_eval_p + normalized_eval_r)
          )
      logger.info("F1: %.5f" % f1)
      results["normalized_f1"] = f1
      return results
  
imports: import os
  import argparse
  import faiss
  import pickle
  import torch
  import json
  import sys
  import io
  import random
  import time
  import traceback
  import numpy
  from scipy.special import softmax, expit
  import torch.nn.functional
  from multiprocessing.pool import ThreadPool
  from tqdm import tqdm, trange
  from collections import OrderedDict
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
  from pytorch_transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
  from pytorch_transformers.optimization import WarmupLinearSchedule
  from pytorch_transformers.tokenization_bert import BertTokenizer
  from elq.biencoder.biencoder import BiEncoderRanker
  from elq.vcg_utils.measures import entity_linking_tp_with_overlap
  import logging
  import elq.candidate_ranking.utils
  from elq.biencoder.data_process import process_mention_data
  from blink.biencoder.zeshel_utils import DOC_PATH, WORLDS, world_to_id
  from blink.common.optimizer import get_bert_optimizer
  from elq.common.params import ElqParser
  from elq.index.faiss_indexer import DenseFlatIndexer, DenseHNSWFlatIndexer, DenseIVFFlatIndexer
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(reranker, eval_dataloader, params, device, logger, cand_encs, faiss_index, get_losses) and returns: results