code: def validate_batch(self, batch):
      self.optim.zero_grad()
      device = next(self.coarse_net.parameters()).device
      assert self.coarse_net.training is False
      if self.fine_net is not None:
          assert self.fine_net.training is False
      with torch.no_grad():
          (
              coarse_rgb_loss,
              coarse_empty_space_loss,
              fine_rgb_loss,
              fine_empty_space_loss,
          ) = self.loss_func(batch, device)
          rgb_loss = coarse_rgb_loss + fine_rgb_loss
          empty_space_loss = (
              coarse_empty_space_loss + fine_empty_space_loss
          )
          total_loss = rgb_loss + empty_space_loss
      return {
          "coarse_rgb_loss": coarse_rgb_loss.data.item(),
          "coarse_empty_space_loss": coarse_empty_space_loss.data.item(),
          "fine_rgb_loss": fine_rgb_loss.data.item(),
          "fine_empty_space_loss": fine_empty_space_loss.data.item(),
          "rgb_loss": rgb_loss.data.item(),
          "empty_space_loss": empty_space_loss.data.item(),
          "total_loss": total_loss.data.item(),
      }
  
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 3 for calling the NeRFTrainer obj's underlying member method code with example usage: obj.validate_batch(batch) and returns: dict