code: def validation_step(self, batch, batch_idx):
      # save image
      images, depths = self._shared_predict(batch)
  
      save_path = os.path.join(self.trainer.log_dir, "validation")
      os.makedirs(save_path, exist_ok=True)
      for i, (image, depth) in enumerate(zip(images, depths)):
          # Save image
          cv2.imwrite(
              os.path.join(
                  save_path,
                  f"{self.current_epoch:04d}_{self.global_step:04d}_{self.global_rank:04d}_{batch_idx:04d}_{i:04d}_rgb.png",
              ),
              cv2.cvtColor(image, cv2.COLOR_RGB2BGR),
          )
          # Save depth
          cv2.imwrite(
              os.path.join(
                  save_path,
                  f"{self.current_epoch:04d}_{self.global_step:04d}_{self.global_rank:04d}_{batch_idx:04d}_{i:04d}_depth.png",
              ),
              depth,
          )
  
imports: import os
  import random
  import cv2
  import imageio
  import numpy
  import torch
  from nemo.collections.multimodal.models.nerf.txt2nerf_base import Txt2NerfBase
  from nemo.collections.multimodal.modules.nerf.loss.laplacian_smooth_loss import LaplacianSmoothLoss
  from nemo.collections.multimodal.modules.nerf.loss.normal_consistency_loss import NormalConsistencyLoss
  from nemo.collections.multimodal.modules.nerf.materials.materials_base import ShadingEnum
  from nemo.core import optim
label_desc: Use image node_id 3 for calling the DreamFusion obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) without return types