class_docstr: Runs the training of a graph classification model using the augmented
  data generated by the already trained generator. Check
  :obj:`examples.auggraph.GraphAug.run_aug_cls` for examples on how to run
  this augmented classifier model.
  
  Args:
      data_root_path (string): Directory where datasets should be saved.
      dataset_name (:class:`dig.auggraph.method.GraphAug.constants.enums.DatasetName`):
          Name of the graph dataset.
      conf (dict): Hyperparameters for the model. Check
          :obj:`examples.auggraph.GraphAug.conf.aug_cls_conf` for examples on
          how to define the conf dictionary for the augmented classifier
          model.
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code: def _train_epoch(self, loader, optimizer):
      self.model.train()
      for data_batch in loader:
          data_batch = data_batch.to(self.device)
          optimizer.zero_grad()
          try:
              output = self.model(data_batch)
          except:
              print(data_batch.x.shape, data_batch.edge_index.shape)
              print(data_batch.batch)
              exit()
          loss = F.nll_loss(output, data_batch.y)
          loss.backward()
          optimizer.step()
  
imports: import os
  import torch
  import torch.nn.functional
  import numpy
  from torch_geometric.loader import DataLoader
  from torch_geometric.datasets import TUDataset
  from sklearn.model_selection import KFold, train_test_split
  from dig.auggraph.method.GraphAug.model import GIN, GCN
  from dig.auggraph.dataset.aug_dataset import DegreeTrans, Subset, AUG_trans
  from dig.auggraph.method.GraphAug.aug import Augmenter
  from dig.auggraph.method.GraphAug.constants import *
label_desc: Use image node_id 5 for calling the RunnerAugCls obj's underlying member method code with example usage: obj._train_epoch(loader, optimizer) without return types