class_docstr: Runs the training of a reward generation model which will be able to
  distinguish between graphs with different labels. Check
  :obj:`examples.auggraph.GraphAug.run_reward_gen` for examples on how
  to run the reward generation 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.reward_gen_conf` for examples
          on how to define the conf dictionary for the reward generator.
code: def __init__(self, data_root_path, dataset_name, conf):
      self.conf = conf
      self._get_dataset(data_root_path, dataset_name)
      self.model = self._get_model()
      self.device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
      self.dataset_name = dataset_name
  
file_path: DIG/dig/auggraph/method/GraphAug/runner_reward_gen.py
imports: import os
  import torch
  import torch.nn.functional
  from torch_geometric.loader import DataLoader
  from torch_geometric.datasets import TUDataset
  from .model import RewardGenModel
  from dig.auggraph.dataset.aug_dataset import DegreeTrans, TripleSet
  from dig.auggraph.method.GraphAug.constants import *
label_desc: Use image node_id 1 to create a new RunnerRewardGen object from inherited base classes: object with example: obj = RunnerRewardGen(data_root_path, dataset_name, conf)