code: def eval(self, model, mode="val"):
      from evaluation import compute_tree_accuracy
  
      model.eval()
      reference_list = []
      candidate_list = []
      data_loader = (
          self.test_data_loader
          if mode == "test"
          else self.valid_data_loader
      )
      for data in tqdm(data_loader, desc="Eval: "):
          eval_input_graph, _, batch_original_tree_list = (
              data["graph_data"],
              data["dec_tree_batch"],
              data["original_dec_tree_batch"],
          )
          eval_input_graph = eval_input_graph.to(self.device)
          oov_dict = self.prepare_ext_vocab(
              eval_input_graph, self.src_vocab
          )
  
          if self.use_copy:
              assert len(batch_original_tree_list) == 1
              reference = oov_dict.get_symbol_idx_for_list(
                  batch_original_tree_list[0].split()
              )
              eval_vocab = oov_dict
          else:
              assert len(batch_original_tree_list) == 1
              reference = model.tgt_vocab.get_symbol_idx_for_list(
                  batch_original_tree_list[0].split()
              )
              eval_vocab = self.tgt_vocab
  
          candidate = model.translate(
              eval_input_graph,
              oov_dict=oov_dict,
              use_beam_search=True,
              beam_size=self.opt["beam_size"],
          )
  
          candidate = [int(c) for c in candidate]
          num_left_paren = sum(
              1
              for c in candidate
              if eval_vocab.idx2symbol[int(c)] == "("
          )
          num_right_paren = sum(
              1
              for c in candidate
              if eval_vocab.idx2symbol[int(c)] == ")"
          )
          diff = num_left_paren - num_right_paren
          if diff > 0:
              for _ in range(diff):
                  candidate.append(
                      self.test_data_loader.tgt_vocab.symbol2idx[")"]
                  )
          elif diff < 0:
              candidate = candidate[:diff]
          # ref_str = convert_to_string(reference, eval_vocab)
          # cand_str = convert_to_string(candidate, eval_vocab)
  
          reference_list.append(reference)
          candidate_list.append(candidate)
      eval_acc = compute_tree_accuracy(
          candidate_list, reference_list, eval_vocab
      )
      print("{} accuracy = {:.3f}\n".format(mode, eval_acc))
      return eval_acc
  
imports: import copy
  import random
  import time
  import warnings
  import numpy
  import torch
  import torch.optim
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from graph4nlp.pytorch.datasets.mawps import MawpsDatasetForTree
  from graph4nlp.pytorch.models.graph2tree import Graph2Tree
  from graph4nlp.pytorch.modules.utils.tree_utils import Tree
label_desc: Use image node_id 8 for calling the Mawps obj's underlying member method code with example usage: obj.eval(model, mode) and returns: eval_acc