code: def validate_batch(self, batch):
      self.optim.zero_grad()
      assert self.net.training is False
      with torch.no_grad():
          loss_dict = self.loss_func(batch)
          loss_dict["rgb_loss"] = (
              loss_dict["fine_rgb_loss"]
              + loss_dict["color_range_reg"]
              + loss_dict["lpips_loss"]
          )
          loss_dict["can_loss"] = (
              loss_dict["smpl_sym_reg"] + loss_dict["smpl_shape_reg"]
          )
          loss_dict["total_loss"] = (
              loss_dict["rgb_loss"]
              + loss_dict["can_loss"]
              + loss_dict["mask_loss"]
              + loss_dict["sparsity_reg"]
          )
      return {k: v.data.item() for k, v in loss_dict.items()}
  
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 11 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj.validate_batch(batch) and returns: unknown