code: def save_model(self):
      save_dict = {
          "epoch": self.epoch,
          "iteration": self.iteration,
          "optim_state_dict": self.optim.state_dict(),
          "hybrid_model_state_dict": self.net.state_dict(),
      }
      torch.save(
          save_dict, os.path.join(self.out, "checkpoint.pth.tar")
      )
  
imports: import os
  import math
  import time
  import random
  import tqdm
  import torch
  import torch.nn.functional
  import numpy
  import torchvision.utils
  import igl
  import lpips
  from utils import utils, render_utils, ray_utils
  from trainers import tensorboard_helper
  from options import options
  from cameras.captures import ResizedPinholeCapture
  from cameras.pinhole_camera import PinholeCamera
  from models.vanilla import weight_reset
  from utils.constant import HARD_SURFACE_OFFSET, PATCH_SIZE, PATCH_SIZE_SQUARED, CANONICAL_ZOOM_FACTOR, CANONICAL_CAMERA_DIST
label_desc: Use image node_id 13 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj.save_model() without return types