code: def evaluate(parameters, logger=None):
      reranker = utils.get_reranker(parameters)
  
      if parameters["full_evaluation"]:
          eval_datasets = [
              "aida-A",
              "aida-B",
              "msnbc",
              "aquaint",
              "ace2004",
              "clueweb",
              "wikipedia",
          ]
      else:
          eval_datasets = ["aida-B"]
  
      candidates_key = (
          "pregenerated_candidates"
          if parameters["evaluate_with_pregenerated_candidates"]
          else "candidates"
      )
      gold_key = (
          "pregenerated_gold_pos"
          if parameters["evaluate_with_pregenerated_candidates"]
          else "gold_pos"
      )
  
      number_of_samples_per_dataset = {}
      total_time = 0
  
      for eval_dataset_name in eval_datasets:
          time_start = time.time()
          logger.info(
              "\nEvaluating on the {} dataset".format(eval_dataset_name)
          )
          eval_samples = utils.read_dataset(
              eval_dataset_name,
              parameters["path_to_preprocessed_json_data"],
          )
          eval_samples_filtered = utils.filter_samples(
              eval_samples, parameters["top_k"], gold_key
          )
          logger.info(
              "Retained {} out of {} samples".format(
                  len(eval_samples_filtered), len(eval_samples)
              )
          )
          number_of_samples_per_dataset[eval_dataset_name] = len(
              eval_samples
          )
  
          # if args.num_preprocessing_threads == -1:
          #     eval_data, eval_tensor_data = process_samples_for_model(args.context_key, eval_samples_filtered, tokenizer, args.max_seq_length, logger = logger, top_k = args.top_k, example = False, debug = args.debug, tagged = args.tag_mention, candidates_key = candidates_key, gold_key = gold_key)
          # else:
          #     eval_data, eval_tensor_data = preprocessing_multithreaded(eval_samples_filtered, logger, args, output_dir=True)
  
          (
              eval_data,
              eval_tensor_data,
          ) = reranker._process_mentions_for_model(
              parameters["context_key"],
              eval_samples_filtered,
              reranker.tokenizer,
              parameters["max_seq_length"],
              parameters["top_k"],
              parameters["silent"],
              candidates_key=candidates_key,
              gold_key=gold_key,
              debug=parameters["debug"],
          )
  
          eval_sampler = SequentialSampler(eval_tensor_data)
          eval_dataloader = DataLoader(
              eval_tensor_data,
              sampler=eval_sampler,
              batch_size=parameters["evaluation_batch_size"],
          )
  
          if parameters["output_eval_file"] is None:
              output_eval_file = os.path.join(
                  parameters["path_to_model"], "eval_results.txt"
              )
          else:
              output_eval_file = parameters["output_eval_file"]
  
          result = evaluate_model_on_dataset(
              reranker.model,
              eval_dataloader,
              eval_dataset_name,
              eval_bm45_acc=True,
              device=reranker.device,
              logger=logger,
              path_to_file_to_write_results=output_eval_file,
              number_of_samples=number_of_samples_per_dataset[
                  eval_dataset_name
              ],
          )
  
          execution_time = (time.time() - time_start) / 60
          total_time += execution_time
          if logger != None:
              logger.info(
                  "The execution for dataset {} took {} minutes".format(
                      eval_dataset_name, execution_time
                  )
              )
          else:
              print(
                  "The execution for dataset {} took {} minutes".format(
                      eval_dataset_name, execution_time
                  )
              )
  
      if logger != None:
          logger.info(
              "The execution for dataset {} took {} minutes".format(
                  eval_dataset_name, execution_time
              )
          )
      else:
          print("The evaluation took:", total_time, " minutes")
  
file_path: BLINK/blink/candidate_ranking/evaluate.py
imports: import time
  import utils
  import torch
  import utils
  import argparse
  import os
  from bert_reranking import BertReranker
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(parameters, logger) without return types