code: def __init__(
      self,
      opt,
      coarse_net,
      optimizer,
      criterion,
      train_loader,
      val_loader,
      train_dataset,
      val_dataset,
      fine_net=None,
      penalize_empty_space=0,
  ):
      self.train_dataset = train_dataset
      self.val_dataset = val_dataset
      self.penalize_empty_space = penalize_empty_space
      if opt.empty_space_loss_fn == "l1":
          self.empty_space_loss_fn = F.l1_loss
      elif opt.empty_space_loss_fn == "mse":
          self.empty_space_loss_fn = F.mse_loss
  
      super().__init__(
          opt,
          coarse_net,
          optimizer,
          criterion,
          train_loader,
          val_loader,
          fine_net=fine_net,
      )
  
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 1 to create a new NeRFTrainer object from inherited base classes: base_trainer with example: obj = NeRFTrainer(opt, coarse_net, optimizer, criterion, train_loader, val_loader, train_dataset, val_dataset, fine_net, penalize_empty_space)