code: def _train_batch(self, data, labels):
      logits = self.model(data=data)
      if self.graph_classification:
          loss = self.__loss__(logits, labels)
      else:
          loss = self.__loss__(
              logits[data.train_mask], labels[data.train_mask]
          )
  
      self.optimizer.zero_grad()
      loss.backward()
      torch.nn.utils.clip_grad_value_(
          self.model.parameters(), clip_value=2.0
      )
      self.optimizer.step()
      return loss.item()
  
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 3 for calling the TrainModel obj's underlying member method code with example usage: obj._train_batch(data, labels) and returns: loss