code: def entity_evaluate(model, batches, tot_gold):
      """
      Evaluate the entity model
      """
      logger.info("Evaluating...")
      c_time = time.time()
      cor = 0
      tot_pred = 0
      l_cor = 0
      l_tot = 0
  
      for i in range(len(batches)):
          output_dict = model.run_batch(batches[i], training=False)
          pred_ner = output_dict["pred_ner"]
          for sample, preds in zip(batches[i], pred_ner):
              for gold, pred in zip(sample["spans_label"], preds):
                  l_tot += 1
                  if pred == gold:
                      l_cor += 1
                  if pred != 0 and gold != 0 and pred == gold:
                      cor += 1
                  if pred != 0:
                      tot_pred += 1
  
      acc = l_cor / l_tot
      logger.info("Accuracy: %5f" % acc)
      logger.info(
          "Cor: %d, Pred TOT: %d, Gold TOT: %d"
          % (cor, tot_pred, tot_gold)
      )
      p = cor / tot_pred if cor > 0 else 0.0
      r = cor / tot_gold if cor > 0 else 0.0
      f1 = 2 * (p * r) / (p + r) if cor > 0 else 0.0
      logger.info("P: %.5f, R: %.5f, F1: %.5f" % (p, r, f1))
      logger.info("Used time: %f" % (time.time() - c_time))
      return f1
  
imports: import json
  import argparse
  import os
  import sys
  import random
  import logging
  import time
  from tqdm import tqdm
  import numpy
  from models.const import *
  from models.data_structures import *
  from models.entityModels import *
  from models.entityUtils import *
  from models.relationModels import *
  from models.relationUtils import *
  from transformers import AdamW, get_linear_schedule_with_warmup
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: entity_evaluate(model, batches, tot_gold) and returns: f1