code: def loss_func(self, batch, return_rgb=False):
      device = next(self.net.parameters()).device
      loss_dict = {
          l: torch.tensor(0.0, requires_grad=True).float().to(device)
          for l in LOSS_NAMES
      }
  
      batch = utils.remove_first_axis(batch)
      hit_index = torch.nonzero(batch["is_hit"])[:, 0]
      (
          _,
          fine_bkg_dirs,
          fine_bkg_z_vals,
          fine_bkg_out,
      ) = self._eval_bkg_samples(batch, device)
      (
          _,
          human_dirs,
          human_z_vals,
          can_pts,
          can_dirs,
          human_out,
      ) = self._eval_human_samples(batch, device)
  
      # canonical space should be symmetric in terms of occupancy
      if self.penalize_symmetric_alpha > 0:
          loss_dict["smpl_sym_reg"] = loss_dict[
              "smpl_sym_reg"
          ] + self._smpl_symmetry_regularization(
              can_pts, can_dirs, human_out
          )
  
      # color of the same point should not change too much due to viewing directions
      if self.penalize_color_range > 0:
          loss_dict["color_range_reg"] = loss_dict[
              "color_range_reg"
          ] + self._color_range_regularization(
              can_pts, can_dirs, human_out
          )
  
      # the rendered human should be close to the detected human mask
      # loosely enforced, the penalty linearly decrease during training
      if self.penalize_mask > 0:
          _, _, human_mask, _, _ = render_utils.raw2outputs(
              human_out,
              human_z_vals,
              human_dirs[:, 0, :],
              white_bkg=self.opt.white_bkg,
          )
          loss_dict["mask_loss"] = (
              loss_dict["mask_loss"]
              + F.mse_loss(
                  torch.clamp(human_mask, min=0.0, max=1.0),
                  (1 - batch["is_bkg"]).float().to(device),
              )
              * self.penalize_mask
          )
  
      # alpha inside smpl mesh should be 1
      # alpha outside smpl mesh should be 0
      if self.penalize_smpl_alpha > 0:
          loss_dict["smpl_shape_reg"] = loss_dict[
              "smpl_shape_reg"
          ] + self._smpl_shape_regularization(
              batch, can_pts, can_dirs, human_out
          )
  
      # sharp edge loss + hard surface loss
      if self.penalize_sharp_edge > 0 or self.penalize_hard_surface > 0:
          loss_dict["sparsity_reg"] = loss_dict[
              "sparsity_reg"
          ] + self._sparsity_regularization(device)
  
      # RGB loss
      fine_total_zvals, fine_order = torch.sort(
          torch.cat([fine_bkg_z_vals, human_z_vals], -1), -1
      )
      fine_total_out = torch.cat([fine_bkg_out, human_out], 1)
      _b, _n, _c = fine_total_out.shape
      fine_total_out = fine_total_out[
          torch.arange(_b).view(_b, 1, 1).repeat(1, _n, _c),
          fine_order.view(_b, _n, 1).repeat(1, 1, _c),
          torch.arange(_c).view(1, 1, _c).repeat(_b, _n, 1),
      ]
      fine_rgb_map, _, _, _, _ = render_utils.raw2outputs(
          fine_total_out,
          fine_total_zvals,
          fine_bkg_dirs[:, 0, :],
          white_bkg=self.opt.white_bkg,
      )
      loss_dict["fine_rgb_loss"] = loss_dict[
          "fine_rgb_loss"
      ] + F.mse_loss(
          fine_rgb_map[hit_index.to(device)],
          batch["color"][hit_index].to(device),
      )
  
      # LPIPS loss
      if self.penalize_lpips > 0 and batch["patch_counter"] == 1:
          temp_lpips_loss = (
              self.lpips_loss_fn(
                  fine_rgb_map[:PATCH_SIZE_SQUARED]
                  .reshape(PATCH_SIZE, PATCH_SIZE, -1)
                  .permute(2, 0, 1)
                  * 2
                  - 1,
                  batch["color"][:PATCH_SIZE_SQUARED]
                  .to(device)
                  .reshape(PATCH_SIZE, PATCH_SIZE, -1)
                  .permute(2, 0, 1)
                  * 2
                  - 1,
              )
              * self.penalize_lpips
          )
          assert torch.numel(temp_lpips_loss) == 1
          loss_dict["lpips_loss"] = (
              loss_dict["lpips_loss"] + temp_lpips_loss.flatten()[0]
          )
  
      # restart if the network is dead
      if human_out[..., 3].max() <= 0.0:
          print("bad weights, reinitializing")
          self.net.offset_nets.apply(weight_reset)
          self.net.coarse_human_net.apply(weight_reset)
          loss_dict = {
              l: torch.tensor(0.0, requires_grad=True)
              .float()
              .to(device)
              for l in LOSS_NAMES
          }
      if return_rgb:
          return loss_dict, fine_rgb_map
      else:
          return loss_dict
  
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 10 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj.loss_func(batch, return_rgb) and returns: loss_dict,  fine_rgb_map, loss_dict