code: def eval(self):
      self.model.to(self.device)
      self.model.eval()
      if self.graph_classification:
          losses, accs = [], []
          for batch in self.loader["eval"]:
              batch = batch.to(self.device)
              loss, batch_preds = self._eval_batch(batch, batch.y)
              losses.append(loss)
              accs.append(batch_preds == batch.y)
          eval_loss = torch.tensor(losses).mean().item()
          eval_acc = torch.cat(accs, dim=-1).float().mean().item()
      else:
          data = self.dataset.data.to(self.device)
          eval_loss, preds = self._eval_batch(
              data, data.y, mask=data.val_mask
          )
          eval_acc = (preds == data.y).float().mean().item()
      return eval_loss, eval_acc
  
imports: import os
  import hydra
  import torch
  import shutil
  import warnings
  from torch.optim import Adam
  from omegaconf import OmegaConf
  from benchmarks.xgraph.utils import check_dir
  from benchmarks.xgraph.gnnNets import get_gnnNets
  from benchmarks.xgraph.dataset import get_dataset, get_dataloader
  import torch.nn.functional
  from torch.optim.lr_scheduler import MultiStepLR
  from utils import fix_random_seed
label_desc: Use image node_id 5 for calling the TrainModel obj's underlying member method code with example usage: obj.eval() and returns: eval_loss,  eval_acc