code: def eval(self, config, epoch, data_loader, is_test=False):
      self.model.eval()
  
      pred_result = []
      label_result = []
  
      total_ent_r = 0
      total_ent_p = 0
      total_ent_c = 0
      with torch.no_grad():
          for i, data_batch in tqdm(
              enumerate(data_loader), desc="Evaluating"
          ):
              entity_text = data_batch[-1]
              data_batch = [data.cuda() for data in data_batch[:-1]]
              (
                  bert_inputs,
                  grid_labels,
                  grid_mask2d,
                  pieces2word,
                  dist_inputs,
                  sent_length,
              ) = data_batch
  
              outputs = self.model(
                  bert_inputs,
                  grid_mask2d,
                  dist_inputs,
                  pieces2word,
                  sent_length,
              )
              length = sent_length
  
              grid_mask2d = grid_mask2d.clone()
  
              outputs = torch.argmax(outputs, -1)
              ent_c, ent_p, ent_r, _ = decode(
                  outputs.cpu().numpy(),
                  entity_text,
                  length.cpu().numpy(),
              )
  
              total_ent_r += ent_r
              total_ent_p += ent_p
              total_ent_c += ent_c
  
              grid_labels = (
                  grid_labels[grid_mask2d].contiguous().view(-1)
              )
              outputs = outputs[grid_mask2d].contiguous().view(-1)
  
              label_result.append(grid_labels.cpu())
              pred_result.append(outputs.cpu())
  
      label_result = torch.cat(label_result)
      pred_result = torch.cat(pred_result)
  
      p, r, f1, _ = precision_recall_fscore_support(
          label_result.numpy(), pred_result.numpy(), average="macro"
      )
      e_f1, e_p, e_r = cal_f1(total_ent_c, total_ent_p, total_ent_r)
  
      title = "EVAL" if not is_test else "TEST"
      # logger.info('{} Label F1 {}'.format(title, f1_score(label_result.numpy(),
      #                                                     pred_result.numpy(),
      #                                                     average=None)))
      logger.info(
          "\n{}".format(
              classification_report(
                  label_result.numpy(), pred_result.numpy()
              )
          )
      )
  
      table = pt.PrettyTable(
          ["{} {}".format(title, epoch), "F1", "Precision", "Recall"]
      )
      table.add_row(
          ["Label"] + ["{:3.4f}".format(x) for x in [f1, p, r]]
      )
      table.add_row(
          ["Entity"] + ["{:3.4f}".format(x) for x in [e_f1, e_p, e_r]]
      )
  
      logger.info("\n{}".format(table))
      return e_f1
  
imports: import argparse
  import json
  import numpy
  import prettytable
  import torch
  import torch.autograd
  import torch.nn
  import transformers
  from sklearn.metrics import precision_recall_fscore_support, f1_score, classification_report
  from torch.utils.data import DataLoader
  import hydra
  from deepke.name_entity_re.standard.w2ner import *
  from deepke.name_entity_re.standard.w2ner.utils import *
  from deepke.name_entity_re.standard.tools import *
label_desc: Use image node_id 3 for calling the Trainer obj's underlying member method code with example usage: obj.eval(config, epoch, data_loader, is_test) and returns: e_f1