code: def load_model(self):
      state_dict = torch.load(
          os.path.join(self.save_dir, f"{self.save_name}_best.pth")
      )["net"]
      self.model.load_state_dict(state_dict)
      self.model.to(self.device)
  
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 9 for calling the TrainModel obj's underlying member method code with example usage: obj.load_model() without return types