code: def loss_func(self, batch, device):
      batch = utils.remove_first_axis(batch)
      if self.opt.ablate_nerft:
          coarse_time = batch["viewf_list"].repeat(
              1, self.opt.samples_per_ray
          )[..., None]
          fine_time = batch["viewf_list"].repeat(
              1,
              self.opt.samples_per_ray
              + self.opt.importance_samples_per_ray,
          )[..., None]
      else:
          coarse_time, fine_time = None, None
      samples = ray_utils.ray_to_samples(
          batch,
          self.opt.samples_per_ray,
          perturb=self.opt.perturb,
          append_t=coarse_time,
      )
      _b, _n, _ = samples[0].shape
      pts = samples[0].to(device)
      dirs = samples[1].to(device)
      z_vals = samples[2].to(device)
  
      out = self.coarse_net(pts, dirs)
      rgb_map, _, _, weights, _ = render_utils.raw2outputs(
          out,
          z_vals,
          dirs[:, 0, :],
          raw_noise_std=self.opt.raw_noise_std,
          white_bkg=self.opt.white_bkg,
      )
      coarse_rgb_loss = F.mse_loss(rgb_map, batch["color"].to(device))
      coarse_empty_space_loss = torch.zeros_like(coarse_rgb_loss)
      if self.penalize_empty_space > 0:
          depth = batch["depth"][:, None].repeat(1, _n).to(device)
          closer_mask = z_vals < (depth * self.opt.margin)
          coarse_empty_space_loss += (
              self.empty_space_loss_fn(
                  torch.tanh(torch.relu(out[closer_mask][:, 3])),
                  torch.zeros_like(out[closer_mask][:, 3]),
              )
              * self.penalize_empty_space
          )
      if self.fine_net is not None:
          F_pts, F_dirs, F_z_vals = ray_utils.ray_to_importance_samples(
              batch,
              z_vals,
              weights,
              self.opt.importance_samples_per_ray,
              device=device,
              append_t=fine_time,
          )
          F_out = self.fine_net(F_pts, F_dirs)
          F_rgb_map, _, _, F_weights, _ = render_utils.raw2outputs(
              F_out,
              F_z_vals,
              F_dirs[:, 0, :],
              raw_noise_std=self.opt.raw_noise_std,
              white_bkg=self.opt.white_bkg,
          )
          fine_rgb_loss = F.mse_loss(
              F_rgb_map, batch["color"].to(device)
          )
          fine_empty_space_loss = torch.zeros_like(fine_rgb_loss)
          if self.penalize_empty_space > 0:
              F_depth = (
                  batch["depth"][:, None]
                  .repeat(1, F_z_vals.shape[1])
                  .to(device)
              )
              F_closer_mask = F_z_vals < (F_depth * self.opt.margin)
              fine_empty_space_loss += (
                  self.empty_space_loss_fn(
                      torch.tanh(
                          torch.relu(F_out[F_closer_mask][:, 3])
                      ),
                      torch.zeros_like(F_out[F_closer_mask][:, 3]),
                  )
                  * self.penalize_empty_space
              )
      if out[..., 3].max() <= 0.0 or F_out[..., 3].max() <= 0.0:
          print("bad weights, reinitializing")
          self.coarse_net.apply(weight_reset)
          if self.fine_net is not None:
              self.fine_net.apply(weight_reset)
          (
              coarse_rgb_loss,
              coarse_empty_space_loss,
              fine_rgb_loss,
              fine_empty_space_loss,
          ) = [
              torch.tensor(0.0, requires_grad=True).float().to(device)
          ] * 4
      return (
          coarse_rgb_loss,
          coarse_empty_space_loss,
          fine_rgb_loss,
          fine_empty_space_loss,
      )
  
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 2 for calling the NeRFTrainer obj's underlying member method code with example usage: obj.loss_func(batch, device) and returns: coarse_rgb_loss,  coarse_empty_space_loss,  fine_rgb_loss,  fine_empty_space_loss