code: def save_model(self):
      save_dict = {
          "epoch": self.epoch,
          "iteration": self.iteration,
          "optim_state_dict": self.optim.state_dict(),
          "coarse_model_state_dict": self.coarse_net.state_dict(),
      }
      if self.fine_net is not None:
          save_dict[
              "fine_model_state_dict"
          ] = self.fine_net.state_dict()
      torch.save(
          save_dict, os.path.join(self.out, "checkpoint.pth.tar")
      )
  
imports: import os
  import time
  from utils.render_utils import render_vanilla
  import tqdm
  import torch
  import torch.nn.functional
  import torchvision.utils
  import numpy
  from utils import utils, render_utils, ray_utils
  from trainers import base_trainer, tensorboard_helper
  from models.vanilla import weight_reset
label_desc: Use image node_id 5 for calling the NeRFTrainer obj's underlying member method code with example usage: obj.save_model() without return types