code: def _eval_batch(self, data, labels, **kwargs):
      self.model.eval()
      logits = self.model(data)
      if self.graph_classification:
          loss = self.__loss__(logits, labels)
      else:
          mask = kwargs.get("mask")
          if mask is None:
              warnings.warn("The node mask is None")
              mask = torch.ones(labels.shape[0])
          loss = self.__loss__(logits[mask], labels[mask])
      loss = loss.item()
      preds = logits.argmax(-1)
      return loss, preds
  
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 4 for calling the TrainModel obj's underlying member method code with example usage: obj._eval_batch(data, labels) and returns: loss,  preds