code: def validation_step(self, batch, batch_idx):
      loss, *_ = self.shared_step(batch)
  
      for t in self.noisy_acc:
          _, logits, _, targets = self.shared_step(batch, t)
          self.noisy_acc[t]["acc@1"].append(
              self.compute_top_k(logits, targets, k=1, reduction="mean")
          )
          self.noisy_acc[t]["acc@5"].append(
              self.compute_top_k(logits, targets, k=5, reduction="mean")
          )
  
      return loss
  
imports: import os
  from copy import deepcopy
  from glob import glob
  import lightning.pytorch
  import torch
  from einops import rearrange
  from ldm.lr_scheduler import LambdaLinearScheduler
  from ldm.models.diffusion.ddpm import LatentDiffusion
  from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
  from ldm.util import default, ismap, log_txt_as_img
  from natsort import natsorted
  from omegaconf import OmegaConf
  from torch.nn import functional
  from torch.optim import AdamW
  from torch.optim.lr_scheduler import LambdaLR
label_desc: Use image node_id 16 for calling the NoisyLatentImageClassifier obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: loss