code: def push_validation_data(self, validation_data):
      render = vutils.make_grid(
          validation_data["render"],
          nrow=2,
          normalize=True,
          scale_each=True,
      )
      tb_datapack = tensorboard_helper.TensorboardDatapack()
      tb_datapack.set_training(False)
      tb_datapack.set_iteration(self.iteration)
      for key in validation_data.keys():
          if "loss" in key or "reg" in key:
              if key == "lpips_loss" and validation_data[key] == 0:
                  pass
              else:
                  tb_datapack.add_scalar(
                      {f"val_loss/{key}": validation_data[key]}
                  )
      tb_datapack.add_image({"render/val": render})
      self.tb_pusher.push_to_tensorboard(tb_datapack)
  
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 14 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj.push_validation_data(validation_data) without return types